Method and system for microbiome-derived diagnostics and therapeutics for locomotor system conditions

ABSTRACT

A method for at least one of characterizing, diagnosing, and treating a locomotor system condition in at least a subject, the method comprising: receiving an aggregate set of biological samples from a population of subjects; generating at least one of a microbiome composition dataset and a microbiome functional diversity dataset for the population of subjects; generating a characterization of the locomotor system condition based upon features extracted from at least one of the microbiome composition dataset and the microbiome functional diversity dataset; based upon the characterization, generating a therapy model configured to correct the locomotor system condition; and at an output device associated with the subject, promoting a therapy to the subject based upon the characterization and the therapy model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. application Ser. No.14/919,614 filed 21 Oct. 2015, which claims the benefit of U.S.Provisional Application Ser. No. 62/066,369 filed 21 Oct. 2014, U.S.Provisional Application Ser. No. 62/087,551 filed 4 Dec. 2014, U.S.Provisional Application Ser. No. 62/092,999 filed 17 Dec. 2014, U.S.Provisional Application Ser. No. 62/147,376 filed 14 Apr. 2015, U.S.Provisional Application Ser. No. 62/147,212 filed 14 Apr. 2015, U.S.Provisional Application Ser. No. 62/147,362 filed 14 Apr. 2015, U.S.Provisional Application Ser. No. 62/146,855 filed 13 Apr. 2015, and U.S.Provisional Application Ser. No. 62/206,654 filed 18 Aug. 2015, whichare each incorporated in its entirety herein by this reference.

This application also claims the benefit of U.S. Provisional ApplicationSer. No. 62/147,102 filed 14 Apr. 2015, U.S. Provisional ApplicationSer. No. 62/147,321 filed 14 Apr. 2015, U.S. Provisional ApplicationSer. No. 62/147,324 filed 14 Apr. 2015, U.S. Provisional ApplicationSer. No. 62/147,287 filed 14 Apr. 2015, and U.S. Provisional ApplicationSer. No. 62/147,314 filed 14 Apr. 2015, which are each incorporated inits entirety herein by this reference.

TECHNICAL FIELD

This invention relates generally to the field of locomotor system healthand more specifically to a new and useful method and system formicrobiome-derived diagnostics and therapeutics in the field oflocomotor system health.

BACKGROUND

A microbiome is an ecological community of commensal, symbiotic, andpathogenic microorganisms that are associated with an organism. Thehuman microbiome comprises as many microbial cells as human cellspresent in the entire human body, but characterization of the humanmicrobiome is still in nascent stages due to limitations in sampleprocessing techniques, genetic analysis techniques, and resources forprocessing large amounts of data. Nonetheless, the microbiome issuspected to play at least a partial role in a number ofhealth/disease-related states (e.g., preparation for childbirth,gastrointestinal disorders, etc.).

Given the profound implications of the microbiome in affecting asubject's health, efforts related to the characterization of themicrobiome, the generation of insights from the characterization, andthe generation of therapeutics configured to rectify states of dysbiosisshould be pursued. Current methods and systems for analyzing themicrobiomes of humans and providing therapeutic measures based on gainedinsights have, however, left many questions unanswered. In particular,methods for characterizing certain health conditions and therapies(e.g., probiotic therapies) tailored to specific subjects have not beenviable due to limitations in current technologies.

As such, there is a need in the field of microbiology for a new anduseful method and system for characterizing locomotor system conditionsin an individualized and population-wide manner. This invention createssuch a new and useful method and system.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1A is a flowchart of an embodiment of a method for characterizing amicrobiome-derived condition and identifying therapeutic measures;

FIG. 1B is a flowchart of an embodiment of a method for generatingmicrobiome-derived diagnostics;

FIG. 2 depicts an embodiment of a method and system for generatingmicrobiome-derived diagnostics and therapeutics;

FIG. 3 depicts variations of a portion of an embodiment of a method forgenerating microbiome-derived diagnostics and therapeutics;

FIG. 4 depicts a variation of a process for generation of a model in anembodiment of a method and system for generating microbiome-deriveddiagnostics and therapeutics;

FIG. 5 depicts variations of mechanisms by which probiotic-basedtherapies operate in an embodiment of a method for characterizing ahealth condition; and

FIG. 6 depicts examples of therapy-related notification provision in anexample of a method for generating microbiome-derived diagnostics andtherapeutics.

DESCRIPTION OF THE EMBODIMENTS

The following description of the embodiments of the invention is notintended to limit the invention to these embodiments, but rather toenable any person skilled in the art to make and use this invention.

1. Method for Characterizing a Microbiome-Derived Condition andIdentifying Therapeutic Measures

As shown in FIG. 1A, a first method 100 for diagnosing and treating alocomotor system condition comprises: receiving an aggregate set ofsamples from a population of subjects S110; characterizing a microbiomecomposition and/or functional features for each of the aggregate set ofsamples associated with the population of subjects, thereby generatingat least one of a microbiome composition dataset and a microbiomefunctional diversity dataset for the population of subjects S120;receiving a supplementary dataset, associated with at least a subset ofthe population of subjects, wherein the supplementary dataset isinformative of characteristics associated with the locomotor systemcondition S130; and transforming the supplementary dataset and featuresextracted from at least one of the microbiome composition dataset andthe microbiome functional diversity dataset into a characterizationmodel of the locomotor system condition S140. In some variations, thefirst method 100 can further include: based upon the characterization,generating a therapy model configured to improve a state of thelocomotor system condition S150.

The first method 100 functions to generate models that can be used tocharacterize and/or diagnose subjects according to at least one of theirmicrobiome composition and functional features (e.g., as a clinicaldiagnostic, as a companion diagnostic, etc.), and provide therapeuticmeasures (e.g., probiotic-based therapeutic measures, phage-basedtherapeutic measures, small-molecule-based therapeutic measures,prebiotic-based therapeutic measures, clinical measures, etc.) tosubjects based upon microbiome analysis for a population of subjects. Assuch, data from the population of subjects can be used to characterizesubjects according to their microbiome composition and/or functionalfeatures, indicate states of health and areas of improvement based uponthe characterization(s), and promote one or more therapies that canmodulate the composition of a subject's microbiome toward one or more ofa set of desired equilibrium states.

In variations, the method 100 can be used to promote targeted therapiesto subjects suffering from a locomotor system condition, disorder, oradverse state, wherein the locomotor system condition affects themusculoskeletal system of the subject in some manner, and producesphysiological effects in relation to one or more of: ability, motorcontrol, mobility, range of motion, activity level, pain, inflammation,joint damage, muscle strain, fatigue, and any other suitablephysiological effect. In these variations, diagnostics associated withthe locomotor system condition can be typically assessed using one ormore of: imaging based method, stress testing, motion testing, biopsy,joint fluid assessment, and any other standard method. In specificexamples, the method 100 can be used for characterization of and/ortherapeutic intervention for one or more of: rheumatic arthritis,reactive arthritis, any other arthritic disorder, gout, and aspondylitic disorder. Additionally or alternatively, the method 100 canbe used for characterization of and/or therapeutic intervention for oneor more locomotor system-associated conditions including: obesity,stroke, Still's disease, a psoriatic disorder, Ehlers-Danlos Syndrome,Haemochromatosis, Hepatitis, Lyme disease, Sjogren's disease,Hashimoto's Thyroiditis, Sarcoidosis, Whipple's disease, and any otherassociated condition. As such, the method 100 can be used tocharacterize locomotor system conditions, disorders, and/or adversestates in an entirely non-typical method. In particular, the inventorspropose that characterization of the microbiome of individuals can beuseful for predicting the likelihood of occurrence of locomotor systemconditions in subjects. Such characterizations can also be useful forscreening for locomotor system conditions and/or determining a course oftreatment for an individual human with a locomotor system condition. Forexample, by deep sequencing bacterial DNAs from diseased and healthysubjects, the inventors propose that features associated with certainmicrobiome compositional and/or functional features (e.g., the amount ofcertain bacteria and/or bacterial sequences corresponding to certaingenetic pathways) can be used to predict the presence or absence of alocomotor system condition. The bacteria and genetic pathways in somecases are present in a certain abundance in individuals having variouslocomotor system conditions as discussed in more detail below whereasthe bacteria and genetic pathways are at a statistically differentabundance in individuals not having the locomotor system condition.

As such, in some embodiments, outputs of the first method 100 can beused to generate diagnostics and/or provide therapeutic measures for asubject based upon an analysis of the subject's microbiome compositionand/or functional features of the subject's microbiome. Thus, as shownin FIG. 1B, a second method 200 derived from at least one output of thefirst method 100 can include: receiving a biological sample from asubject S210; characterizing the subject with a form of a locomotorsystem condition based upon processing a microbiome dataset derived fromthe biological sample S220; and promoting a therapy to the subject withthe locomotor system condition based upon the characterization and thetherapy model S230. Variations of the method 100 can further facilitatemonitoring and/or adjusting of therapies provided to a subject, forinstance, through reception, processing, and analysis of additionalsamples from a subject throughout the course of therapy. Embodiments,variations, and examples of the second method 200 are described in moredetail below.

The methods thus 100, 200 function to generate models that can be usedto classify individuals and/or provide therapeutic measures (e.g.,therapy recommendations, therapies, therapy regimens, etc.) toindividuals based upon microbiome analysis for a population ofindividuals. As such, data from the population of individuals can beused to generate models that can classify individuals according to theirmicrobiome compositions (e.g., as a diagnostic measure), indicate statesof health and areas of improvement based upon the classification(s),and/or provide therapeutic measures that can push the composition of anindividual's microbiome toward one or more of a set of improvedequilibrium states. Variations of the second method 200 can furtherfacilitate monitoring and/or adjusting of therapies provided to anindividual, for instance, through reception, processing, and analysis ofadditional samples from an individual throughout the course of therapy.

In one application, at least one of the methods 100, 200 is implemented,at least in part, at a system 300, as shown in FIG. 2, that receives abiological sample derived from the subject (or an environment associatedwith the subject) by way of a sample reception kit, and processes thebiological sample at a processing system implementing a characterizationprocess and a therapy model configured to positively influence amicroorganism distribution in the subject (e.g., human, non-humananimal, environmental ecosystem, etc.). In variations of theapplication, the processing system can be configured to generate and/orimprove the characterization process and the therapy model based uponsample data received from a population of subjects. The method 100 can,however, alternatively be implemented using any other suitable system(s)configured to receive and process microbiome-related data of subjects,in aggregation with other information, in order to generate models formicrobiome-derived diagnostics and associated therapeutics. Thus, themethod 100 can be implemented for a population of subjects (e.g.,including the subject, excluding the subject), wherein the population ofsubjects can include patients dissimilar to and/or similar to thesubject (e.g., in health condition, in dietary needs, in demographicfeatures, etc.). Thus, information derived from the population ofsubjects can be used to provide additional insight into connectionsbetween behaviors of a subject and effects on the subject's microbiome,due to aggregation of data from a population of subjects.

Thus, the methods 100, 200 can be implemented for a population ofsubjects (e.g., including the subject, excluding the subject), whereinthe population of subjects can include subjects dissimilar to and/orsimilar to the subject (e.g., health condition, in dietary needs, indemographic features, etc.). Thus, information derived from thepopulation of subjects can be used to provide additional insight intoconnections between behaviors of a subject and effects on the subject'smicrobiome, due to aggregation of data from a population of subjects.

1.1 First Method: Sample Handling

Block Silo recites: receiving an aggregate set of biological samplesfrom a population of subjects, which functions to enable generation ofdata from which models for characterizing subjects and/or providingtherapeutic measures to subjects can be generated. In Block S110,biological samples are preferably received from subjects of thepopulation of subjects in a non-invasive manner. In variations,non-invasive manners of sample reception can use any one or more of: apermeable substrate (e.g., a swab configured to wipe a region of asubject's body, toilet paper, a sponge, etc.), a non-permeable substrate(e.g., a slide, tape, etc.), a container (e.g., vial, tube, bag, etc.)configured to receive a sample from a region of a subject's body, andany other suitable sample-reception element. In a specific example,samples can be collected from one or more of a subject's nose, skin,genitals, mouth, and gut in a non-invasive manner (e.g., using a swaband a vial). However, one or more biological samples of the set ofbiological samples can additionally or alternatively be received in asemi-invasive manner or an invasive manner. In variations, invasivemanners of sample reception can use any one or more of: a needle, asyringe, a biopsy element, a lance, and any other suitable instrumentfor collection of a sample in a semi-invasive or invasive manner. Inspecific examples, samples can comprise blood samples, plasma/serumsamples (e.g., to enable extraction of cell-free DNA), synovial fluid,and tissue samples.

In the above variations and examples, samples can be taken from thebodies of subjects without facilitation by another entity (e.g., acaretaker associated with an individual, a health care professional, anautomated or semi-automated sample collection apparatus, etc.), or canalternatively be taken from bodies of individuals with the assistance ofanother entity. In one example, wherein samples are taken from thebodies of subjects without facilitation by another entity in the sampleextraction process, a sample-provision kit can be provided to a subject.In the example, the kit can include one or more swabs for sampleacquisition, one or more containers configured to receive the swab(s)for storage, instructions for sample provision and setup of a useraccount, elements configured to associate the sample(s) with the subject(e.g., barcode identifiers, tags, etc.), and a receptacle that allowsthe sample(s) from the individual to be delivered to a sample processingoperation (e.g., by a mail delivery system). In another example, whereinsamples are extracted from the user with the help of another entity, oneor more samples can be collected in a clinical or research setting froma subject (e.g., during a clinical appointment).

In Block S110, the aggregate set of biological samples is preferablyreceived from a wide variety of subjects, and can involve samples fromhuman subjects and/or non-human subjects. In relation to human subjects,Block Silo can include receiving samples from a wide variety of humansubjects, collectively including subjects of one or more of: differentdemographics (e.g., genders, ages, marital statuses, ethnicities,nationalities, socioeconomic statuses, sexual orientations, etc.),different health conditions (e.g., health and disease states), differentliving situations (e.g., living alone, living with pets, living with asignificant other, living with children, etc.), different dietary habits(e.g., omnivorous, vegetarian, vegan, sugar consumption, acidconsumption, etc.), different behavioral tendencies (e.g., levels ofphysical activity, drug use, alcohol use, etc.), different levels ofmobility (e.g., related to distance traveled within a given timeperiod), biomarker states (e.g., cholesterol levels, lipid levels,etc.), weight, height, body mass index, genotypic factors, and any othersuitable trait that has an effect on microbiome composition. As such, asthe number of subjects increases, the predictive power of feature-basedmodels generated in subsequent blocks of the method 100 increases, inrelation to characterizing a variety of subjects based upon theirmicrobiomes. Additionally or alternatively, the aggregate set ofbiological samples received in Block Silo can include receivingbiological samples from a targeted group of similar subjects in one ormore of: demographic traits, health conditions, living situations,dietary habits, behavior tendencies, levels of mobility, age range(e.g., pediatric, adulthood, geriatric), and any other suitable traitthat has an effect on microbiome composition. Additionally oralternatively, the methods 100, 200 can be adapted to characterizeconditions typically detected by way of lab tests (e.g., polymerasechain reaction based tests, cell culture based tests, blood tests,biopsies, chemical tests, etc.), physical detection methods (e.g.,manometry), medical history based assessments, behavioral assessments,and imagenology based assessments. Additionally or alternatively, themethods 100, 200 can be adapted to characterization of acute conditions,chronic conditions, conditions with difference in prevalence fordifferent demographics, conditions having characteristic disease areas(e.g., the head, the gut, endocrine system diseases, the heart, nervoussystem diseases, respiratory diseases, immune system diseases,circulatory system diseases, renal system diseases, locomotor systemdiseases, etc.), and comorbid conditions.

In some embodiments, receiving the aggregate set of biological samplesin Block Silo can be performed according to embodiments, variations, andexamples of sample reception as described in U.S. application Ser. No.14/593,424 filed on 9 Jan. 2015 and entitled “Method and System forMicrobiome Analysis”, which is incorporated herein in its entirety bythis reference. However, receiving the aggregate set of biologicalsamples in Block Silo can additionally or alternatively be performed inany other suitable manner. Furthermore, some variations of the firstmethod 100 can omit Block S110, with processing of data derived from aset of biological samples performed as described below in subsequentblocks of the method 100.

1.2 First Method: Sample Analysis, Microbiome Composition, andFunctional Aspects

Block S120 recites: characterizing a microbiome composition and/orfunctional features for each of the aggregate set of biological samplesassociated with a population of subjects, thereby generating at leastone of a microbiome composition dataset and a microbiome functionaldiversity dataset for the population of subjects. Block S120 functionsto process each of the aggregate set of biological samples, in order todetermine compositional and/or functional aspects associated with themicrobiome of each of a population of subjects. Compositional andfunctional aspects can include compositional aspects at themicroorganism level, including parameters related to distribution ofmicroorganisms across different groups of kingdoms, phyla, classes,orders, families, genera, species, subspecies, strains, infraspeciestaxon (e.g., as measured in total abundance of each group, relativeabundance of each group, total number of groups represented, etc.),and/or any other suitable taxa. Compositional and functional aspects canalso be represented in terms of operational taxonomic units (OTUs).Compositional and functional aspects can additionally or alternativelyinclude compositional aspects at the genetic level (e.g., regionsdetermined by multilocus sequence typing, 16S sequences, 18S sequences,ITS sequences, other genetic markers, other phylogenetic markers, etc.).Compositional and functional aspects can include the presence or absenceor the quantity of genes associated with specific functions (e.g.,enzyme activities, transport functions, immune activities, etc.).Outputs of Block S120 can thus be used to provide features of interestfor the characterization process of Block S140, wherein the features canbe microorganism-based (e.g., presence of a genus of bacteria),genetic-based (e.g., based upon representation of specific geneticregions and/or sequences) and/or functional-based (e.g., presence of aspecific catalytic activity, presence of metabolic pathways, etc.).

In one variation, Block S120 can include characterization of featuresbased upon identification of phylogenetic markers derived from bacteriaand/or archaea in relation to gene families associated with one or moreof: ribosomal protein S₂, ribosomal protein S₃, ribosomal protein S₅,ribosomal protein S₇, ribosomal protein S₈, ribosomal protein S₉,ribosomal protein S₁₀, ribosomal protein S₁₁, ribosomal protein S₁₂/S₂₃,ribosomal protein S₁₃, ribosomal protein S₁₅P/S₁₃e, ribosomal proteinS₁₇, ribosomal protein S₁₉, ribosomal protein L₁, ribosomal protein L₂,ribosomal protein L₃, ribosomal protein L₄/L₁e, ribosomal protein L₅,ribosomal protein L₆, ribosomal protein L₁₀, ribosomal protein L₁₁,ribosomal protein L₁₃, ribosomal protein L₁₄b/L₂₃e, ribosomal proteinL₁₅, ribosomal protein L₁₆/L₁₀E, ribosomal protein L₁₈P/L₅E, ribosomalprotein L₂₂, ribosomal protein L₂₄, ribosomal protein L₂₅/L₂₃, ribosomalprotein L₂₉, translation elongation factor EF-2, translation initiationfactor IF-2, metalloendopeptidase, ffh signal recognition particleprotein, phenylalanyl-tRNA synthetase alpha subunit, phenylalanyl-tRNAsynthetase beta subunit, tRNA pseudouridine synthase B, porphobilinogendeaminase, phosphoribosylformylglycinamidine cyclo-ligase, andribonuclease HII. However, the markers can include any other suitablemarker(s)

Characterizing the microbiome composition and/or functional features foreach of the aggregate set of biological samples in Block S120 thuspreferably includes a combination of sample processing techniques (e.g.,wet laboratory techniques) and computational techniques (e.g., utilizingtools of bioinformatics) to quantitatively and/or qualitativelycharacterize the microbiome and functional features associated with eachbiological sample from a subject or population of subjects.

In variations, sample processing in Block S120 can include any one ormore of: lysing a biological sample, disrupting membranes in cells of abiological sample, separation of undesired elements (e.g., RNA,proteins) from the biological sample, purification of nucleic acids(e.g., DNA) in a biological sample, amplification of nucleic acids fromthe biological sample, further purification of amplified nucleic acidsof the biological sample, and sequencing of amplified nucleic acids ofthe biological sample. Thus, portions of Block S120 can be implementedusing embodiments, variations, and examples of the sample handlingnetwork and/or computing system as described in U.S. application Ser.No. 14/593,424 filed on 9 Jan. 2015 and entitled “Method and System forMicrobiome Analysis”, which is incorporated herein in its entirety bythis reference. Thus the computing system implementing one or moreportions of the method 100 can be implemented in one or more computingsystems, wherein the computing system(s) can be implemented at least inpart in the cloud and/or as a machine (e.g., computing machine, server,mobile computing device, etc.) configured to receive a computer-readablemedium storing computer-readable instructions. However, Block S120 canbe performed using any other suitable system(s).

In variations, lysing a biological sample and/or disrupting membranes incells of a biological sample preferably includes physical methods (e.g.,bead beating, nitrogen decompression, homogenization, sonication), whichomit certain reagents that produce bias in representation of certainbacterial groups upon sequencing. Additionally or alternatively, lysingor disrupting in Block S120 can involve chemical methods (e.g., using adetergent, using a solvent, using a surfactant, etc.). Additionally oralternatively, lysing or disrupting in Block S120 can involve biologicalmethods. In variations, separation of undesired elements can includeremoval of RNA using RNases and/or removal of proteins using proteases.In variations, purification of nucleic acids can include one or more of:precipitation of nucleic acids from the biological samples (e.g., usingalcohol-based precipitation methods), liquid-liquid based purificationtechniques (e.g., phenol-chloroform extraction), chromatography-basedpurification techniques (e.g., column adsorption), purificationtechniques involving use of binding moiety-bound particles (e.g.,magnetic beads, buoyant beads, beads with size distributions,ultrasonically responsive beads, etc.) configured to bind nucleic acidsand configured to release nucleic acids in the presence of an elutionenvironment (e.g., having an elution solution, providing a pH shift,providing a temperature shift, etc.), and any other suitablepurification techniques.

In variations, performing an amplification operation S123 on purifiednucleic acids can include performing one or more of: polymerase chainreaction (PCR)-based techniques (e.g., solid-phase PCR, RT-PCR, qPCR,multiplex PCR, touchdown PCR, nanoPCR, nested PCR, hot start PCR, etc.),helicase-dependent amplification (HDA), loop mediated isothermalamplification (LAMP), self-sustained sequence replication (₃SR), nucleicacid sequence based amplification (NASBA), strand displacementamplification (SDA), rolling circle amplification (RCA), ligase chainreaction (LCR), and any other suitable amplification technique. Inamplification of purified nucleic acids, the primers used are preferablyselected to prevent or minimize amplification bias, as well asconfigured to amplify nucleic acid regions/sequences (e.g., of the 16Sregion, the 18S region, the ITS region, etc.) that are informativetaxonomically, phylogenetically, for diagnostics, for formulations(e.g., for probiotic formulations), and/or for any other suitablepurpose. Thus, universal primers (e.g., a F27-R338 primer set for 16SRNA, a F515-R806 primer set for 16S RNA, etc.) configured to avoidamplification bias can be used in amplification. Primers used invariations of Block Silo can additionally or alternatively includeincorporated barcode sequences specific to each biological sample, whichcan facilitate identification of biological samples post-amplification.Primers used in variations of Block Silo can additionally oralternatively include adaptor regions configured to cooperate withsequencing techniques involving complementary adaptors (e.g., accordingto protocols for Illumina Sequencing).

Identification of a primer set for a multiplexed amplification operationcan be performed according to embodiments, variations, and examples ofmethods described in U.S. App. No. 62/206,654 filed 18 Aug. 2015 andentitled “Method and System for Multiplex Primer Design”, which isherein incorporated in its entirety by this reference. Performing amultiplexed amplification operation using a set of primers in Block S123can additionally or alternatively be performed in any other suitablemanner.

Additionally or alternatively, as shown in FIG. 3, Block S120 canimplement any other step configured to facilitate processing (e.g.,using a Nextera kit) for performance of a fragmentation operation S122(e.g., fragmentation and tagging with sequencing adaptors) incooperation with the amplification operation S123 (e.g., S122 can beperformed after S123, S122 can be performed before S123, S122 can beperformed substantially contemporaneously with S123, etc.) Furthermore,Blocks S122 and/or S123 can be performed with or without a nucleic acidextraction step. For instance, extraction can be performed prior toamplification of nucleic acids, followed by fragmentation, and thenamplification of fragments. Alternatively, extraction can be performed,followed by fragmentation and then amplification of fragments. As such,in some embodiments, performing an amplification operation in Block S123can be performed according to embodiments, variations, and examples ofamplification as described in U.S. application Ser. No. 14/593,424 filedon 9 Jan. 2015 and entitled “Method and System for Microbiome Analysis”.Furthermore, amplification in Block S123 can additionally oralternatively be performed in any other suitable manner.

In a specific example, amplification and sequencing of nucleic acidsfrom biological samples of the set of biological samples includes:solid-phase PCR involving bridge amplification of DNA fragments of thebiological samples on a substrate with oligo adapters, whereinamplification involves primers having a forward index sequence (e.g.,corresponding to an Illumina forward index for MiSeq/NextSeq/HiSeqplatforms) or a reverse index sequence (e.g., corresponding to anIllumina reverse index for MiSeq/NextSeq/HiSeq platforms), a forwardbarcode sequence or a reverse barcode sequence, a transposase sequence(e.g., corresponding to a transposase binding site forMiSeq/NextSeq/HiSeq platforms), a linker (e.g., a zero, one, or two-basefragment configured to reduce homogeneity and improve sequence results),an additional random base, and a sequence for targeting a specifictarget region (e.g., 16S region, 18S region, ITS region). Amplificationand sequencing can further be performed on any suitable amplicon, asindicated throughout the disclosure. In the specific example, sequencingcomprises Illumina sequencing (e.g., with a HiSeq platform, with a MiSeqplatform, with a NextSeq platform, etc.) using a sequencing-by-synthesistechnique. Additionally or alternatively, any other suitable nextgeneration sequencing technology (e.g., PacBio platform, MinIONplatform, Oxford Nanopore platform, etc.) can be used. Additionally oralternatively, any other suitable sequencing platform or method can beused (e.g., a Roche 454 Life Sciences platform, a Life TechnologiesSOLiD platform, etc.). In examples, sequencing can include deepsequencing to quantify the number of copies of a particular sequence ina sample and then also be used to determine the relative abundance ofdifferent sequences in a sample. Deep sequencing refers to highlyredundant sequencing of a nucleic acid sequence, for example such thatthe original number of copies of a sequence in a sample can bedetermined or estimated. The redundancy (i.e., depth) of the sequencingis determined by the length of the sequence to be determined (X), thenumber of sequencing reads (N), and the average read length (L). Theredundancy is then NxL/X. The sequencing depth can be, or be at leastabout 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,56, 57, 58, 59, 60, 70, 80, 90, 100, 110, 120, 130, 150, 200, 300, 500,500, 700, 1000, 2000, 3000, 4000, 5000 or more.

Some variations of sample processing in Block S120 can include furtherpurification of amplified nucleic acids (e.g., PCR products) prior tosequencing, which functions to remove excess amplification elements(e.g., primers, dNTPs, enzymes, salts, etc.). In examples, additionalpurification can be facilitated using any one or more of: purificationkits, buffers, alcohols, pH indicators, chaotropic salts, nucleic acidbinding filters, centrifugation, and any other suitable purificationtechnique.

In variations, computational processing in Block S120 can include anyone or more of: performing a sequencing analysis operation S124including identification of microbiome-derived sequences (e.g., asopposed to subject sequences and contaminants), performing an alignmentand/or mapping operation S125 of microbiome-derived sequences (e.g.,alignment of fragmented sequences using one or more of single-endedalignment, ungapped alignment, gapped alignment, pairing), andgenerating features S126 derived from compositional and/or functionalaspects of the microbiome associated with a biological sample.

Performing the sequencing analysis operation S124 with identification ofmicrobiome-derived sequences can include mapping of sequence data fromsample processing to a subject reference genome (e.g., provided by theGenome Reference Consortium), in order to remove subject genome-derivedsequences. Unidentified sequences remaining after mapping of sequencedata to the subject reference genome can then be further clustered intooperational taxonomic units (OTUs) based upon sequence similarity and/orreference-based approaches (e.g., using VAMPS, using MG-RAST, usingQIIME databases), aligned (e.g., using a genome hashing approach, usinga Needleman-Wunsch algorithm, using a Smith-Waterman algorithm), andmapped to reference bacterial genomes (e.g., provided by the NationalCenter for Biotechnology Information), using an alignment algorithm(e.g., Basic Local Alignment Search Tool, FPGA accelerated alignmenttool, BWT-indexing with BWA, BWT-indexing with SOAP, BWT-indexing withBowtie, etc.). Mapping of unidentified sequences can additionally oralternatively include mapping to reference archaeal genomes, viralgenomes and/or eukaryotic genomes. Furthermore, mapping of taxa can beperformed in relation to existing databases, and/or in relation tocustom-generated databases.

Additionally or alternatively, in relation to generating a microbiomefunctional diversity dataset, Block S120 can include extractingcandidate features associated with functional aspects of one or moremicrobiome components of the aggregate set of biological samples S127,as indicated in the microbiome composition dataset. Extracting candidatefunctional features can include identifying functional featuresassociated with one or more of: prokaryotic clusters of orthologousgroups of proteins (COGs); eukaryotic clusters of orthologous groups ofproteins (KOGs); any other suitable type of gene product; an RNAprocessing and modification functional classification; a chromatinstructure and dynamics functional classification; an energy productionand conversion functional classification; a cell cycle control andmitosis functional classification; an amino acid metabolism andtransport functional classification; a nucleotide metabolism andtransport functional classification; a carbohydrate metabolism andtransport functional classification; a coenzyme metabolism functionalclassification; a lipid metabolism functional classification; atranslation functional classification; a transcription functionalclassification; a replication and repair functional classification; acell wall/membrane/envelop biogenesis functional classification; a cellmotility functional classification; a post-translational modification,protein turnover, and chaperone functions functional classification; aninorganic ion transport and metabolism functional classification; asecondary metabolites biosynthesis, transport and catabolism functionalclassification; a signal transduction functional classification; anintracellular trafficking and secretion functional classification; anuclear structure functional classification; a cytoskeleton functionalclassification; a general functional prediction only functionalclassification; and a function unknown functional classification; andany other suitable functional classification.

Additionally or alternatively, extracting candidate functional featuresin Block S127 can include identifying functional features associatedwith one or more of: systems information (e.g., pathway maps forcellular and organismal functions, modules or functional units of genes,hierarchical classifications of biological entities); genomicinformation (e.g., complete genomes, genes and proteins in the completegenomes, orthologous groups of genes in the complete genomes); chemicalinformation (e.g., chemical compounds and glycans, chemical reactions,enzyme nomenclature); health information (e.g., human diseases, approveddrugs, crude drugs and health-related substances); metabolism pathwaymaps; genetic information processing (e.g., transcription, translation,replication and repair, etc.) pathway maps; environmental informationprocessing (e.g., membrane transport, signal transduction, etc.) pathwaymaps; cellular processes (e.g., cell growth, cell death, cell membranefunctions, etc.) pathway maps; organismal systems (e.g., immune system,endocrine system, nervous system, etc.) pathway maps; human diseasepathway maps; drug development pathway maps; and any other suitablepathway map.

In extracting candidate functional features, Block S127 can compriseperforming a search of one or more databases, such as the KyotoEncyclopedia of Genes and Genomes (KEGG) and/or the Clusters ofOrthologous Groups (COGs) database managed by the National Center forBiotechnology Information (NCBI). Searching can be performed based uponresults of generation of the microbiome composition dataset from one ormore of the set of aggregate biological samples and/or sequencing ofmaterial from the set of samples. In more detail, Block S127 can includeimplementation of a data-oriented entry point to a KEGG databaseincluding one or more of a KEGG pathway tool, a KEGG BRITE tool, a KEGGmodule tool, a KEGG orthology tool, a KEGG genome tool, a KEGG genestool, a KEGG compound tool, a KEGG glycan tool, a KEGG reaction tool, aKEGG disease tool, a KEGG drug tool, a KEGG medicus tool, Searching canadditionally or alternatively be performed according to any othersuitable filters. Additionally or alternatively, Block S127 can includeimplementation of an organism-specific entry point to a KEGG databaseincluding a KEGG organisms tool. Additionally or alternatively, BlockS127 can include implementation of an analysis tool including one ormore of: a KEGG mapper tool that maps KEGG pathway, BRITE, or moduledata; a KEGG atlas tool for exploring KEGG global maps, a BlastKOALAtool for genome annotation and KEGG mapping, a BLAST/FASTA sequencesimilarity search tool, and a SIMCOMP chemical structure similaritysearch tool. In specific examples, Block S127 can include extractingcandidate functional features, based on the microbiome compositiondataset, from a KEGG database resource and a COG database resource;however, Block S127 can comprise extracting candidate functionalfeatures in any other suitable manner. For instance, Block S127 caninclude extracting candidate functional features, including functionalfeatures derived from a Gene Ontology functional classification, and/orany other suitable features.

In one example, a taxonomic group can include one or more bacteria andtheir corresponding reference sequences. A sequence read can be assignedbased on the alignment to a taxonomic group when the sequence readaligns to a reference sequence of the taxonomic group. A functionalgroup can correspond to one or more genes labeled as having a similarfunction. Thus, a functional group can be represented by referencesequences of the genes in the functional group, where the referencesequences of a particular gene can correspond to various bacteria. Thetaxonomic and functional groups can collectively be referred to assequence groups, as each group includes one or more reference sequencesthat represent the group. A taxonomic group of multiple bacteria can berepresented by multiple reference sequence, e.g., one reference sequenceper bacteria species in the taxonomic group. Embodiments can use thedegree of alignment of a sequence read to multiple reference sequencesto determine which sequence group to assign the sequence read based onthe alignment.

1.2.1 Examples and Variations: Sequence Group Corresponds to TaxonomicGroup

A taxonomic group can correspond to any set of one or more referencesequences for one or more loci (e.g., genes) that represent thetaxonomic group. Any given level of a taxonomic hierarchy would includea plurality of taxonomic groups. For instance, a reference sequence inthe one group at the genus level can be in another group at the familylevel.

The RAV can correspond to the proportion of reads assigned to aparticular taxonomic group. The proportion can be relative to variousdenominator values, e.g., relative to all of the sequence reads,relative to all assigned to at least one group (taxonomic orfunctional), or all assigned to for a given level in the hierarchy. Thealignment can be implemented in any manner that can assign a sequenceread to a particular taxonomic group.

For example, based on the mappings to the reference sequence(s) in the16S region, a taxonomic group with the best match for the alignment canbe identified. The RAV can then be determined for that taxonomic groupusing the number of sequence reads (or votes of sequence reads) for aparticular sequence group divided by the number of sequence readsidentified as being bacterial, which may be for a specific region oreven for a given level of a hierarchy.

1.2.2 Examples and Variations: Sequence Group Corresponds to FunctionalGroup or Gene

Instead of or in addition to determining a count of the sequence readsthat correspond to a particular taxonomic group, embodiments can use acount of a number of sequence reads that correspond to a particular geneor a collection of genes having an annotation of a particular function,where the collection is called a functional group. The RAV can bedetermined in a similar manner as for a taxonomic group. For example,functional group can include a plurality of reference sequencescorresponding to one or more genes of the functional group. Referencesequences of multiple bacteria for a same gene can correspond to a samefunctional group. Then, to determine the RAV, the number of sequencereads assigned to the functional group can be used to determine aproportion for the functional group.

The use of a function group, which may include a single gene, can helpto identify situations where there is a small change (e.g., increase) inmany taxonomic groups such that the change is too small to bestatistically significant. But, the changes may all be for a same geneor set of genes of a same functional group, and thus the change for thatfunctional group can be statistically significant, even though thechanges for the taxonomic groups may not be significant. The reverse canbe true of a taxonomic group being more predictive than a particularfunctional group, e.g., when a single taxonomic group includes manygenes that have change by a relatively small amount.

As an example, if 10 taxonomic groups increase by 10%, the statisticalpower to discriminate between the two groups may be low when eachtaxonomic group is analyzed individually. But, if the increase is allfor genes(s) of a same functional group, then the increase would be100%, or a doubling of the proportion for that taxonomic group. Thislarge increase would have a much larger statistical power fordiscriminating between the two groups. Thus, the functional group canact to provide a sum of small changes for various taxonomic groups. And,small changes for various functional groups, which happen to all be on asame taxonomic group, can sum to provide high statistical power for thatparticular taxonomic group.

The taxonomic groups and functional groups can supplement each other asthe information can be orthogonal, or at least partially orthogonal asthere still may be some relationship between the RAVs of each group. Forexample, the RAVs of one or more taxonomic groups and functional groupscan be used together as multiple features of a feature vector, which isanalyzed to provide a diagnosis, as is described herein. For instance,the feature vector can be compared to a disease signature as part of acharacterization model.

1.2.3 Examples and Variations: Pipeline for Taxonomic Groups

Embodiments can provide a bioinformatics pipeline that taxonomicallyannotates the microorganisms present in a sample. The example annotationpipeline can comprise the following procedures.

In a first block, the samples can be identified and the sequence datacan be loaded. For example, the pipeline can begin with demultiplexedfastq files (or other suitable files) that are the product of pair-endsequencing of amplicons (e.g., of the V₄ region of the 16S gene). Allsamples can be identified for a given input sequencing file, and thecorresponding fastq files can be obtained from the fastq repositoryserver and loaded into the pipeline.

In a second block, the reads can be filtered. For example, a globalquality filtering of reads in the fastq files can accept reads with aglobal Q-score>30. In one implementation, for each read, theper-position Q-scores are averaged, and if the average is equal orhigher than 30, then the read is accepted, else the read is discarded,as is its paired read.

In a third block, primers can be identified and removed. In oneembodiment, only forward reads that contain the forward primer andreverse reads that contain the reverse primer (allowing annealing ofprimers with up to 5 mismatches or other number of mismatches) arefurther considered. Primers and any sequences 5′ to them are removedfrom the reads. The 125 bp (or other suitable number) towards the 3′ ofthe forward primer are considered from the forward reads, and only 124bp (or other suitable number) towards the 3′ of the reverse primer areconsidered for the reverse reads. All processed forward reads that are<125 bp and reverse reads that are <124 bp are eliminated from furtherprocessing as are their paired reads.

In a fourth block, the forward and reverse reads can be written to files(e.g., FASTA files). For example, the forward and reverse reads thatremained paired can be used to generate files that contain 125 bp fromthe forward read, concatenated to 124 bp from the reverse read (in thereverse complement direction).

In a fifth block, the sequence reads can be clustered, e.g., to identifychimeric sequences or determine a consensus sequence for a bacterium.For example, the sequences in the files can be subjected to clusteringusing the Swarm algorithm with a distance of 1. This treatment allowsthe generation of cluster composed of a central biological entity,surrounded by sequences which are 1 mutation away from the biologicalentity, which are less abundant and the result of the normal basecalling error associated to high throughput sequencing. Singletons areremoved from further analyses. In the remaining clusters, the mostabundant sequence per cluster is then used as the representative andassigned the counts of all members in the cluster.

In a sixth block, chimeric sequences can be removed. For example,amplification of gene superfamilies can produce the formation ofchimeric DNA sequences. These result from a partial PCR product from onemember of the superfamily that anneals and extends over a differentmember of the superfamily in a subsequent cycle of PCR. In order toremove chimeric DNA sequences, some embodiments can use the VSEARCHchimera detection algorithm with the de novo option and standardparameters. This algorithm uses abundance of PCR products to identifyreference “real” sequences as those most abundant, and chimeric productsas those less abundant and displaying local similarity to two or more ofthe reference sequences. All chimeric sequences can be removed fromfurther analysis.

In a seventh block, taxonomy annotation can be assigned to sequencesusing sequence identity searches. To assign taxonomy to the sequencesthat have passed all filters above, some embodiments can performidentity searches against a database that contains bacterial strains(e.g., reference sequences) annotated to phylum, class, order, family,genus and species level, or any other taxonomic levels. The mostspecific level of taxonomy annotation for a sequence can be kept, giventhat higher order taxonomy designations for a lower level taxonomy levelcan be inferred. The sequence identity search can be performed using thealgorithm VSEARCH with parameters (maxaccepts=0, maxrejects=0, id=1)that allow an exhaustive exploration of the reference database used.Decreasing values of sequence identity can be used to assign sequencesto different taxonomic groups: >97% sequence identity for assigning to aspecies, >95% sequence identity for assigning to a genus, >90% forassigning to family, >85% for assigning to order, >80% for assigning toclass, and >77% for assigning to phylum.

In an eighth block, relative abundances of each taxa can be estimatedand output to a database. For example, once all sequences have been usedto identify sequences in the reference database, relative abundance pertaxa can be determined by dividing the count of all sequences that areassigned to the same taxonomic group by the total number of reads thatpassed filters, e.g., were assigned. Results can be uploaded to databasetables that are used as repository for the taxonomic annotation data.

1.2.4 Examples and Variations: Pipeline for Functional Groups

For functional groups, the process can proceed as follows.

In a first step, sample OTUs (Operational Taxonomic Units) can be found.This may occur after the sixth block from above. After the sixth blockabove, sequences can be clustered, e.g., based on sequence identity(e.g., 97% sequence identity).

In a second step, a taxonomy can be assigned, e.g., by comparing OTUswith reference sequences of known taxonomy. The comparison can be basedon sequence identity (e.g., 97%).

In a third step, taxonomic abundance can be adjusted for 16S copynumber, or whatever genomic regions may be analyzed. Different speciesmay have different number of copies of the 16S gene, so those possessinghigher number of copies will have more 16S material for PCRamplification at same number of cells than other species. Therefore,abundance can be normalized by adjusting the number of 16S copies.

In a fourth step, a pre-computed genomic lookup table can be used torelate taxonomy to functions, and amount of function. For example, apre-computed genomic lookup table that shows the number of genes forimportant KEGG or COG functional categories per taxonomic group can beused to estimate the abundance of those functional categories based onthe normalized 16S abundance data.

Upon identification of represented groups of microorganisms of themicrobiome associated with a biological sample and/or identification ofcandidate functional aspects (e.g., functions associated with themicrobiome components of the biological samples), generating featuresderived from compositional and/or functional aspects of the microbiomeassociated with the aggregate set of biological samples can beperformed.

In one variation, generating features can include generating featuresderived from multilocus sequence typing (MLST), which can be performedexperimentally at any stage in relation to implementation of the methods100, 200, in order to identify markers useful for characterization insubsequent blocks of the method 100. Additionally or alternatively,generating features can include generating features that describe thepresence or absence of certain taxonomic groups of microorganisms,and/or ratios between exhibited taxonomic groups of microorganisms.Additionally or alternatively, generating features can includegenerating features describing one or more of: quantities of representedtaxonomic groups, networks of represented taxonomic groups, correlationsin representation of different taxonomic groups, interactions betweendifferent taxonomic groups, products produced by different taxonomicgroups, interactions between products produced by different taxonomicgroups, ratios between dead and alive microorganisms (e.g., fordifferent represented taxonomic groups, based upon analysis of RNAs),phylogenetic distance (e.g., in terms of Kantorovich-Rubinsteindistances, Wasserstein distances etc.), any other suitable taxonomicgroup-related feature(s), any other suitable genetic or functionalfeature(s).

Additionally or alternatively, generating features can includegenerating features describing relative abundance of differentmicroorganism groups, for instance, using a sparCC approach, usingGenome Relative Abundance and Average size (GAAS) approach and/or usinga Genome Relative Abundance using Mixture Model theory (GRAMMy) approachthat uses sequence-similarity data to perform a maximum likelihoodestimation of the relative abundance of one or more groups ofmicroorganisms. Additionally or alternatively, generating features caninclude generating statistical measures of taxonomic variation, asderived from abundance metrics. Additionally or alternatively,generating features can include generating features derived fromrelative abundance factors (e.g., in relation to changes in abundance ofa taxon, which affects abundance of other taxa). Additionally oralternatively, generating features can include generation of qualitativefeatures describing presence of one or more taxonomic groups, inisolation and/or in combination. Additionally or alternatively,generating features can include generation of features related togenetic markers (e.g., representative 16S, 18S, and/or ITS sequences)characterizing microorganisms of the microbiome associated with abiological sample. Additionally or alternatively, generating featurescan include generation of features related to functional associations ofspecific genes and/or organisms having the specific genes. Additionallyor alternatively, generating features can include generation of featuresrelated to pathogenicity of a taxon and/or products attributed to ataxon. Block S120 can, however, include generation of any other suitablefeature(s) derived from sequencing and mapping of nucleic acids of abiological sample. For instance, the feature(s) can be combinatory(e.g., involving pairs, triplets), correlative (e.g., related tocorrelations between different features), and/or related to changes infeatures (i.e., temporal changes, changes across sample sites, spatialchanges, etc.). Features can, however, be generated in any othersuitable manner in Block S120.

1.3 First Method: Supplementary Data

Block S130 recites: receiving a supplementary dataset, associated withat least a subset of the population of subjects, wherein thesupplementary dataset is informative of characteristics associated withthe locomotor system condition. The supplementary dataset can thus beinformative of presence of the condition within the population ofsubjects. Block S130 functions to acquire additional data associatedwith one or more subjects of the set of subjects, which can be used totrain and/or validate the characterization processes performed in BlockS140. In Block S130, the supplementary dataset preferably includessurvey-derived data, but can additionally or alternatively include anyone or more of: contextual data derived from sensors (e.g.,accelerometers, gyroscopes, sensors for monitoring activity, etc.),medical data (e.g., current and historical medical data associated witha locomotor system condition), and any other suitable type of data. Invariations of Block S130 including reception of survey-derived data, thesurvey-derived data preferably provides physiological, demographic, andbehavioral information in association with a subject. Physiologicalinformation can include information related to physiological features(e.g., height, weight, body mass index, body fat percent, body hairlevel, etc.). Demographic information can include information related todemographic features (e.g., gender, age, ethnicity, marital status,number of siblings, socioeconomic status, sexual orientation, etc.).Behavioral information can include information related to one or moreof: health conditions (e.g., health and disease states), livingsituations (e.g., living alone, living with pets, living with asignificant other, living with children, etc.), dietary habits (e.g.,omnivorous, vegetarian, vegan, sugar consumption, acid consumption,etc.), behavioral tendencies (e.g., levels of physical activity, druguse, alcohol use, etc.), different levels of mobility (e.g., related todistance traveled within a given time period), different levels ofsexual activity (e.g., related to numbers of partners and sexualorientation), and any other suitable behavioral information.Survey-derived data can include quantitative data and/or qualitativedata that can be converted to quantitative data (e.g., using scales ofseverity, mapping of qualitative responses to quantified scores, etc.).

In facilitating reception of survey-derived data, Block S130 can includeproviding one or more surveys to a subject of the population ofsubjects, or to an entity associated with a subject of the population ofsubjects. Surveys can be provided in person (e.g., in coordination withsample provision and reception from a subject), electronically (e.g.,during account setup by a subject, at an application executing at anelectronic device of a subject, at a web application accessible throughan internet connection, etc.), and/or in any other suitable manner.

Additionally or alternatively, portions of the supplementary datasetreceived in Block S130 can be derived from sensors associated with thesubject(s) (e.g., sensors of wearable computing devices, sensors ofmobile devices, biometric sensors associated with the user, etc.). Assuch, Block S130 can include receiving one or more of: physicalactivity- or physical action-related data (e.g., accelerometer andgyroscope data from a mobile device or wearable electronic device of asubject), environmental data (e.g., temperature data, elevation data,climate data, light parameter data, etc.), patient nutrition ordiet-related data (e.g., data from food establishment check-ins, datafrom spectrophotometric analysis, etc.), biometric data (e.g., datarecorded through sensors within the patient's mobile computing device,data recorded through a wearable or other peripheral device incommunication with the patient's mobile computing device), location data(e.g., using GPS elements), and any other suitable data. Additionally oralternatively, portions of the supplementary dataset can be derived frommedical record data and/or clinical data of the subject(s). As such,portions of the supplementary dataset can be derived from one or moreelectronic health records (EHRs) of the subject(s).

Additionally or alternatively, the supplementary dataset of Block S130can include any other suitable diagnostic information (e.g., clinicaldiagnosis information), which can be combined with analyses derived fromfeatures to support characterization of subjects in subsequent blocks ofthe method 100. For instance, information derived from a colonoscopy,biopsy, blood test, diagnostic imaging, survey-related information, andany other suitable test can be used to supplement Block S130.

1.4 First Method: Characterizations of the Locomotor System Condition

Block S140 recites: transforming the supplementary dataset and featuresextracted from at least one of the microbiome composition dataset andthe microbiome functional diversity dataset into a characterizationmodel of the locomotor system condition. Block S140 functions to performa characterization process for identifying features and/or featurecombinations that can be used to characterize subjects or groups withthe locomotor system condition based upon their microbiome compositionand/or functional features. Additionally or alternatively, thecharacterization process can be used as a diagnostic tool that cancharacterize a subject (e.g., in terms of behavioral traits, in terms ofmedical conditions, in terms of demographic traits, etc.) based upontheir microbiome composition and/or functional features, in relation toother health condition states, behavioral traits, medical conditions,demographic traits, and/or any other suitable traits. Suchcharacterization can then be used to suggest or provide personalizedtherapies by way of the therapy model of Block S150.

In performing the characterization process, Block S140 can usecomputational methods (e.g., statistical methods, machine learningmethods, artificial intelligence methods, bioinformatics methods, etc.)to characterize a subject as exhibiting features characteristic of agroup of subjects with the locomotor system condition.

In one variation, characterization can be based upon features derivedfrom a statistical analysis (e.g., an analysis of probabilitydistributions) of similarities and/or differences between a first groupof subjects exhibiting a target state (e.g., a health condition state)associated with the locomotor system condition, and a second group ofsubjects not exhibiting the target state (e.g., a “normal” state)associated with the locomotor system condition. In implementing thisvariation, one or more of a Kolmogorov-Smirnov (KS) test, a permutationtest, a Cramir-von Mises test, and any other statistical test (e.g.,t-test, Welch's t-test, z-test, chi-squared test, test associated withdistributions, etc.) can be used. In particular, one or more suchstatistical hypothesis tests can be used to assess a set of featureshaving varying degrees of abundance in (or variations across) a firstgroup of subjects exhibiting a target state (i.e., an adverse state)associated with the locomotor system condition and a second group ofsubjects not exhibiting the target state (i.e., having a normal state)associated with the locomotor system condition. In more detail, the setof features assessed can be constrained based upon percent abundanceand/or any other suitable parameter pertaining to diversity inassociation with the first group of subjects and the second group ofsubjects, in order to increase or decrease confidence in thecharacterization. In a specific implementation of this example, afeature can be derived from a taxon of microorganism and/or presence ofa functional feature that is abundant in a certain percentage ofsubjects of the first group and subjects of the second group, wherein arelative abundance of the taxon between the first group of subjects andthe second group of subjects can be determined from one or more of a KStest or a Welch's t-test (e.g., a t-test with a log normaltransformation), with an indication of significance (e.g., in terms ofp-value). Thus, an output of Block S140 can comprise a normalizedrelative abundance value (e.g., 25% greater abundance of a taxon-derivedfeature and/or a functional feature in sick subjects vs. healthysubjects) with an indication of significance (e.g., a p-value of0.0013). Variations of feature generation can additionally oralternatively implement or be derived from functional features ormetadata features (e.g., non-bacterial markers).

In variations and examples, characterization can use the relativeabundance values (RAVs) for populations of subjects that have a disease(condition population) and that do not have the disease (controlpopulation). If the distribution of RAVs of a particular sequence groupfor the condition population is statistically different than thedistribution of RAVs for the control population, then the particularsequence group can be identified for including in a disease signature.Since the two populations have different distributions, the RAV for anew sample for a sequence group in the disease signature can be used toclassify (e.g., determine a probability) of whether the sample does ordoes not have the disease. The classification can also be used todetermine a treatment, as is described herein. A discrimination levelcan be used to identify sequence groups that have a high predictivevalue. Thus, embodiment can filter out taxonomic groups and/orfunctional groups that are not very accurate for providing a diagnosis.

Once RAVs of a sequence group have been determined for the control andcondition populations, various statistical tests can be used todetermine the statistical power of the sequence group for discriminatingbetween disease (condition) and no disease (control). In one embodiment,the Kolmogorov-Smirnov (KS) test can be used to provide a probabilityvalue (p-value) that the two distributions are actually identical. Thesmaller the p-value the greater the probability to correctly identifywhich population a sample belongs. The larger the separation in the meanvalues between the two populations generally results in a smallerp-value (an example of a discrimination level). Other tests forcomparing distributions can be used. The Welch's t-test presumes thatthe distributions are Gaussian, which is not necessarily true for aparticular sequence group. The KS test, as it is a non-parametric test,is well suited for comparing distributions of taxa or functions forwhich the probability distributions are unknown.

The distribution of the RAVs for the control and condition populationscan be analyzed to identify sequence groups with a large separationbetween the two distributions. The separation can be measured as ap-value (See example section). For example, the relative abundancevalues for the control population may have a distribution peaked at afirst value with a certain width and decay for the distribution. And,the condition population can have another distribution that is peaked asecond value that is statistically different than the first value. Insuch an instance, an abundance value of a control sample has a lowerprobability to be within the distribution of abundance valuesencountered for the condition samples. The larger the separation betweenthe two distributions, the more accurate the discrimination is fordetermining whether a given sample belongs to the control population orthe condition population. As is discussed later, the distributions canbe used to determine a probability for an RAV as being in the controlpopulation and determine a probability for the RAV being in thecondition population, where sequence groups associated with the largestpercentage difference between two means have the smallest p-value,signifying a greater separation between the two populations.

In performing the characterization process, Block S140 can additionallyor alternatively transform input data from at least one of themicrobiome composition dataset and microbiome functional diversitydataset into feature vectors that can be tested for efficacy inpredicting characterizations of the population of subjects. Data fromthe supplementary dataset can be used to inform characterizations of thelocomotor system condition, wherein the characterization process istrained with a training dataset of candidate features and candidateclassifications to identify features and/or feature combinations thathave high degrees (or low degrees) of predictive power in accuratelypredicting a classification. As such, refinement of the characterizationprocess with the training dataset identifies feature sets (e.g., ofsubject features, of combinations of features) having high correlationwith presence of the locomotor system condition.

In variations, feature vectors effective in predicting classificationsof the characterization process can include features related to one ormore of: microbiome diversity metrics (e.g., in relation to distributionacross taxonomic groups, in relation to distribution across archaeal,bacterial, viral, and/or eukaryotic groups), presence of taxonomicgroups in one's microbiome, representation of specific genetic sequences(e.g., 16S sequences) in one's microbiome, relative abundance oftaxonomic groups in one's microbiome, microbiome resilience metrics(e.g., in response to a perturbation determined from the supplementarydataset), abundance of genes that encode proteins or RNAs with givenfunctions (enzymes, transporters, proteins from the immune system,hormones, interference RNAs, etc.) and any other suitable featuresderived from the microbiome composition dataset, the microbiomefunctional diversity dataset (e.g., COG-derived features, KEGG derivedfeatures, other functional features, etc.), and/or the supplementarydataset. Additionally, combinations of features can be used in a featurevector, wherein features can be grouped and/or weighted in providing acombined feature as part of a feature set. For example, one feature orfeature set can include a weighted composite of the number ofrepresented classes of bacteria in one's microbiome, presence of aspecific genus of bacteria in one's microbiome, representation of aspecific 16S sequence in one's microbiome, and relative abundance of afirst phylum over a second phylum of bacteria. However, the featurevectors can additionally or alternatively be determined in any othersuitable manner.

In examples of Block S140, assuming sequencing has occurred at asufficient depth, one can quantify the number of reads for sequencesindicative of the presence of a feature (e.g., features described inSections 1.4.1-1.4.5 below), thereby allowing one to set a value for anestimated amount of one of the criteria. The number of reads or othermeasures of amount of one of the features can be provided as an absoluteor relative value. An example of an absolute value is the number ofreads of 16S RNA coding sequence reads that map to a specific genus.Alternatively, relative amounts can be determined. An exemplary relativeamount calculation is to determine the amount of 16S RNA coding sequencereads for a particular taxon (e.g., genus, family, order, class, orphylum) relative to the total number of 16S RNA coding sequence readsassigned to the domain. A value indicative of amount of a feature in thesample can then be compared to a cut-off value or a probabilitydistribution in a disease signature for a locomotor system condition.For example, if the disease signature indicates that a relative amountof feature #1 of 50% or more of all features possible at that levelindicates the likelihood of a locomotor system condition, thenquantification of gene sequences associated with feature #1 less than50% in a sample would indicate a higher likelihood of healthy (or atleast not that specific locomotor system condition) and alternatively,quantification of gene sequences associated with feature #1 more than50% in a sample would indicate a higher likelihood of disease.

In examples, the taxonomic groups and/or functional groups can bereferred to as features, or as sequence groups in the context ofdetermining an amount of sequence reads corresponding to a particulargroup (feature). In examples, scoring of a particular bacteria orgenetic pathway can be determined according to a comparison of anabundance value to one or more reference (calibration) abundance valuesfor known samples, e.g., where a detected abundance value less than acertain value is associated with the locomotor system condition inquestion and above the certain value is scored as associated withhealthy, or vice versa depending on the particular criterion. Thescoring for various bacteria or genetic pathways can be combined toprovide a classification for a subject. Furthermore, in the examples,the comparison of an abundance value to one or more reference abundancevalues can include a comparison to a cutoff value determined from theone or more reference values. Such cutoff value(s) can be part of adecision tree or a clustering technique (where a cutoff value is used todetermine which cluster the abundance value(s) belong) that aredetermined using the reference abundance values. The comparison caninclude intermediate determination of other values, (e.g., probabilityvalues). The comparison can also include a comparison of an abundancevalue to a probability distribution of the reference abundance values,and thus a comparison to probability values.

In some embodiments, certain samples may not exhibit any presence of aparticular taxonomic group, or at least not a presence above arelatively low threshold (i.e., a threshold below either of the twodistributions for the control and condition population). Thus, aparticular sequence group may be prevalent in the population, e.g., morethan 30% of the population may have the taxonomic group. Anothersequence group may be less prevalent in the population, e.g., showing upin only 5% of the population. The prevalence (e.g., percentage ofpopulation) of a certain sequence group can provide information as tohow likely the sequence group may be used to determine a diagnosis.

In such an example, the sequence group can be used to determine a statusof the condition (e.g., diagnose for the condition) when the subjectfalls within the 30%. But, when the subject does not fall within the30%, such that the taxonomic group is simply not present, the particulartaxonomic group may not be helpful in determining a diagnosis of thesubject. Thus, whether a particular taxonomic group or functional groupis useful in diagnosing a particular subject can be dependent on whethernucleic acid molecules corresponding to the sequence group are actuallysequenced.

Accordingly, a disease signature can include more sequence groups thatare used for a given subject. As an example, the disease signature caninclude 100 sequence groups, but only 60 of sequence groups may bedetected in a sample. The classification of the subject (including anyprobability for being in the application) would be determined based onthe 60 sequence groups.

In relation to generation of the characterization model, the sequencegroups with high discrimination levels (e.g., low p-values) for a givendisease can be identified and used as part of a characterization model,e.g., which uses a disease signature to determine a probability of asubject having the disease. The disease signature can include a set ofsequence groups as well as discriminating criteria (e.g., cutoff valuesand/or probability distributions) used to provide a classification ofthe subject. The classification can be binary (e.g., disease ornon-disease) or have more classifications (e.g., probability values forhaving the disease or not having the disease). Which sequence groups ofthe disease signature that are used in making a classification bedependent on the specific sequence reads obtained, e.g., a sequencegroup would not be used if no sequence reads were assigned to thatsequence group. In some embodiments, a separate characterization modelcan be determined for different populations, e.g., by geography wherethe subject is currently residing (e.g., country, region, or continent),the generic history of the subject (e.g., ethnicity), or other factors.

1.4.0 Selection of Sequence Groups, Discrimination Criteria SequenceGroups, and Use of Sequence Groups

As mentioned above, sequence groups having at least a specifieddiscrimination level can be selected for inclusion in thecharacterization model. In various embodiments, the specifieddiscrimination level can be an absolute level (e.g., having a p-valuebelow a specified value), a percentage (e.g., being in the top 10% ofdiscriminating levels), or a specified number of the top discriminationlevels (e.g., the top 100 discriminating levels). In some embodiments,the characterization model can include a network graph, where each nodein a graph corresponds to a sequence group having at least a specifieddiscrimination level.

The sequence groups used in a disease signature of a characterizationmodel can also be selected based on other factors. For example, aparticular sequence group may only be detected in a certain percentageof the population, referred to as a coverage percentage. An idealsequence group would be detected in a high percentage of the populationand have a high discriminating level (e.g., a low p-value). A minimumpercentage may be required before adding the sequence group to thecharacterization model for a particular disease. The minimum percentagecan vary based on the accompanying discriminating level. For instance, alower coverage percentage may be tolerated if the discriminating levelis higher. As a further example, 95% of the patients with a conditionmay be classified with one or a combination of a few sequence groups,and the 5% remaining can be explained based on one sequence group, whichrelates to the orthogonality or overlap between the coverage of sequencegroups. Thus, a sequence group that provides discriminating power for 5%of the diseased individuals may be valuable.

Another factor for determining which sequence to include in a diseasesignature of the characterization model is the overlap in the subjectsexhibiting the sequence groups of a disease signature. For example, twosequence groups can both have a high coverage percentage, but sequencegroups may cover the exact same subjects. Thus, adding one of thesequence groups does increase the overall coverage of the diseasesignature. In such a situation, the two sequence groups can beconsidered parallel to each other. Another sequence group can beselected to add to the characterization model based on the sequencegroup covering different subjects than other sequence groups already inthe characterization model. Such a sequence group can be consideredorthogonal to the already existing sequence groups in thecharacterization model.

As examples, selecting a sequence group may consider the followingfactors. A taxa may appear in 100% of healthy individuals and in 100% ofdiseased individuals, but where the distributions are so close in bothgroups, that knowing the relative abundance of that taxa only allows tocatalogue a few individuals as diseased or healthy (i.e. it has a lowdiscriminating level). Whereas, a taxa that appears in only 20% ofhealthy individuals and 30% of diseased individuals can havedistributions of relative abundance that are so different from oneanother, it allows to catalogue 20% of healthy individuals and 30% ofdiseased individuals (i.e. it has a high discriminating level).

In some embodiments, machine learning techniques can allow the automaticidentification of the best combination of features (e.g., sequencegroups). For instance, a Principal Component Analysis can reduce thenumber of features used for classification to only those that are themost orthogonal to each other and can explain most of the variance inthe data. The same is true for a network theory approach, where one cancreate multiple distance metrics based on different features andevaluate which distance metric is the one that best separates diseasedfrom healthy individuals.

The discrimination criteria for the sequence groups included in thedisease signature of a characterization model can be determined based onthe condition distributions and the control distributions for thedisease. For example, a discrimination criterion for a sequence groupcan be a cutoff value that is between the mean values for the twodistributions. As another example, discrimination criteria for asequence group can include probability distributions for the control andcondition populations. The probability distributions can be determinedin a separate manner from the process of determining the discriminationlevel.

The probability distributions can be determined based on thedistribution of RAVs for the two populations. The mean values (or otheraverage or median) for the two populations can be used to center thepeaks of the two probability distributions. For example, if the mean RAVof the condition population is 20% (or 0.2), then the probabilitydistribution for the condition population can have its peak at 20%. Thewidth or other shape parameters (e.g., the decay) can also be determinedbased on the distribution of RAVs for the condition population. The samecan be done for the control population.

The sequence groups included in the disease signature of thecharacterization can be used to classify a new subject. The sequencegroups can be considered features of the feature vector, or the RAVs ofthe sequence groups considered as features of a feature vector, wherethe feature vector can be compared to the discriminating criteria of thedisease signature. For instance, the RAVs of the sequence groups for thenew subject can be compared to the probability distributions for eachsequence group of the disease signature. If an RAV is zero or nearlyzero, then the sequence group may be skipped and not used in theclassification.

The RAVs for sequence groups that are exhibited in the new subject canbe used to determine the classification. For example, the result (e.g.,a probability value) for each exhibited sequence group can be combinedto arrive at the final classification. As another example, clustering ofthe RAVs can be performed, and the clusters can be used to determine aclassification of a condition.

As shown in FIG. 4, in one such alternative variation of Block S140, thecharacterization process can be generated and trained according to arandom forest predictor (RFP) algorithm that combines bagging (i.e.,bootstrap aggregation) and selection of random sets of features from atraining dataset to construct a set of decision trees, T, associatedwith the random sets of features. In using a random forest algorithm, Ncases from the set of decision trees are sampled at random withreplacement to create a subset of decision trees, and for each node, mprediction features are selected from all of the prediction features forassessment. The prediction feature that provides the best split at thenode (e.g., according to an objective function) is used to perform thesplit (e.g., as a bifurcation at the node, as a trifurcation at thenode). By sampling many times from a large dataset, the strength of thecharacterization process, in identifying features that are strong inpredicting classifications can be increased substantially. In thisvariation, measures to prevent bias (e.g., sampling bias) and/or accountfor an amount of bias can be included during processing to increaserobustness of the model.

1.4.1 Gout Characterization

In one implementation, a characterization process of Block S140 basedupon statistical analyses can identify the sets of features that havethe highest correlations with gout, for which one or more therapieswould have a positive effect, based upon an algorithm trained andvalidated with a validation dataset derived from a subset of thepopulation of subjects. In particular, gout in this first variation ismetabolic disorder characterized by recurrent attacks of acuteinflammatory arthritis, as typically assessed based upon identificationof monosodium urate crystals in synovial fluid or a tophus, and bloodtests (e.g., for white blood cell count, electrolytes, renal function,and erythrocyte sedimentation rate). In the first variation, a set offeatures useful for diagnostics associated with gout includes featuresderived from one or more of the following taxa: Defluviitalea (genus),Defluviitaleaceae (family), Eubacterium hallii (species), Coprococcus(genus), Parabacteroides distasonis (species), Erysipelotrichaceae(family), Erysipelotrichia (class), Erysipelotrichales (order),unclassified Clostridiaceae (no rank), Streptococcus thermophilus(species), Christensenellaceae (family), Actinobacteria (phylum),Actinobacteria (phylum), Chlamydiae/Verrucomicrobia group (superphylum),Verrucomicrobia (phylum), Verrucomicrobiae (class), Verrucomicrobiales(order), Verrucomicrobiaceae (family), Tenericutes (phylum),Streptococcaceae (family), Eggerthella (genus), Streptococcus (genus),Actinobacteridae (subclass), Clostridiales incertae sedis (family),Clostridiales Family XI. Incertae Sedis (no rank), Lentisphaerae(phylum), Lentisphaeria (class), Oribacterium (genus), Actinomycetaceae(family), Actinomycineae (suborder), Actinomycetales (order),Corynebacterineae (family), Corynebacteriaceae (family), Corynebacterium(genus), Staphylococcaceae (family).

Additionally or alternatively, the set of features associated with goutcan be derived from one or more of: COG derived features, KEGG L₂, L₃,L₄ derived features, and any other suitable functional features. Thus,characterization of the subject comprises characterization of thesubject as someone with gout based upon detection of one or more of theabove features, in a manner that is an alternative or supplemental totypical methods of diagnosis. In variations of the specific example, theset of features can, however, include any other suitable features usefulfor diagnostics.

1.4.2 Reactive Arthritis Characterization

In another implementation, a characterization process of Block S140based upon statistical analyses can identify the sets of features thathave the highest correlations with reactive arthritis, for which one ormore therapies would have a positive effect, based upon an algorithmtrained and validated with a validation dataset derived from a subset ofthe population of subjects. In particular, reactive arthritis in thisfirst variation is an autoimmune disorder characterized by inflammationof synovial tissues, as typically assessed by presence of arthriticjoints, swabs of mucous membranes, tests of C-reactive protein, andtests of erythrocyte sedimentation rate. In the first variation, a setof features useful for diagnostics associated with reactive arthritisincludes features derived from one or more of the following taxa:Alistipes onderdonkii (species), Alistipes (genus), Rikenellaceae(family), Adlercreutzia (genus), Adlercreutzia equolifaciens (species),Dorea (genus).

Additionally or alternatively, the set of features associated withreactive arthritis can be derived from one or more of: COG derivedfeatures, KEGG L₂, L₃, L₄ derived features, and any other suitablefunctional features. Thus, characterization of the subject comprisescharacterization of the subject as someone with reactive arthritis basedupon detection of one or more of the above features, in a manner that isan alternative or supplemental to typical methods of diagnosis. Invariations of the specific example, the set of features can, however,include any other suitable features useful for diagnostics.

1.4.3 Rheumatoid Arthritis Characterization

In another implementation, a characterization process of Block S140based upon statistical analyses can identify the sets of features thathave the highest correlations with rheumatoid arthritis, for which oneor more therapies would have a positive effect, based upon an algorithmtrained and validated with a validation dataset derived from a subset ofthe population of subjects. In particular, rheumatoid arthritis in thisfirst variation is a systemic inflammatory disorder characterized byinflammation of synovial tissue, as typically assessed with medicalimaging and/or blood tests for presence of rheumatoid factor. In thefirst variation, a set of features useful for diagnostics associatedwith reactive arthritis includes features derived from one or more ofthe following taxa: Bacteroides uniformis (species) and Alistipesonderdonkii (species).

Additionally or alternatively, the set of features associated withrheumatoid arthritis can be derived from one or more of: COG derivedfeatures, KEGG L₂, L₃, L₄ derived features, and any other suitablefunctional features. Thus, characterization of the subject comprisescharacterization of the subject as someone with rheumatoid arthritisbased upon detection of one or more of the above features, in a mannerthat is an alternative or supplemental to typical methods of diagnosis.In variations of the specific example, the set of features can, however,include any other suitable features useful for diagnostics.

1.4.4 Multiple Sclerosis Characterization

In another implementation, a characterization process of Block S140based upon statistical analyses can identify the sets of features thathave the highest correlations with Multiple sclerosis, for which one ormore therapies would have a positive effect, based upon an algorithmtrained and validated with a validation dataset derived from a subset ofthe population of subjects. In particular, Multiple sclerosis in thisfirst variation is an inflammatory disease characterized by damage tonervous system cells and tissue, as typically assessed by medicalimaging and/or testing of cerebrospinal fluid for evidence of chronicinflammation. In the first variation, a set of features useful fordiagnostics associated with Multiple sclerosis includes features derivedfrom one or more of the following taxa: Lactococcus (genus).

Additionally or alternatively, the set of features associated withMultiple sclerosis can be derived from one or more of the followingtaxa: Verrucomicrobiae (class), Verrucomicrobiales (order), Anaerostipes(genus), Lachnospiraceae (family), Cyanobacteria (phylum), Peptococcus(genus), Coprococcus comes (species), Clostridiales bacterium A2-162(species), Prevotellaceae (family), Prevotella (genus),butyrate-producing bacterium L₁₋₉₃ (species), Actinobacillus porcinus(species), Actinobacillus (genus), Pasteurellaceae (family),Pasteurellales (order), and Actinomycetales (order).

Additionally or alternatively, the set of features associated withmultiple sclerosis can be derived from one or more of: COG derivedfeatures, KEGG L₂, L₃, L₄ derived features, and any other suitablefunctional features. Thus, characterization of the subject comprisescharacterization of the subject as someone with Multiple sclerosis basedupon detection of one or more of the above features, in a manner that isan alternative or supplemental to typical methods of diagnosis. Invariations of the specific example, the set of features can, however,include any other suitable features useful for diagnostics.

1.4.5 Parkinson's Disease Characterization

In another implementation, a characterization process of Block S140based upon statistical analyses can identify the sets of features thathave the highest correlations with Parkinson's disease, for which one ormore therapies would have a positive effect, based upon an algorithmtrained and validated with a validation dataset derived from a subset ofthe population of subjects. In particular, Parkinson's disease in thisfirst variation is a degenerative disorder of the nervous systemtypically characterized by assessment of medical history and aneurological examination. In the first variation, a set of featuresuseful for diagnostics associated with Parkinson's disease includesfeatures derived from one or more of the following taxa: Subdoligranulumsp. 4_3_54A2FAA (species), Faecalibacterium (genus), Faecalibacteriumprausnitzii (species), Enterobacteriales (order), Enterobacteriaceae(family), Cyanobacteria (phylum), Anaerostipes (genus), environmentalsamples (no rank), Peptostreptococcaceae (family), Anaerostipes hadrus(species), Bifidobacteriaceae (family), Bifidobacteriales (order),Lactobacillaceae (family), Lactobacillus (genus), Christensenellaceae(family), unclassified Peptostreptococcaceae (no rank),Peptostreptococcaceae bacterium TM₅ (species), and Tenericutes (phylum).

Additionally or alternatively, the set of features associated withParkinson's disease can be derived from one or more of: COG derivedfeatures, KEGG L₂, L₃, L₄ derived features, and any other suitablefunctional features. Thus, characterization of the subject comprisescharacterization of the subject as someone with Parkinson's diseasebased upon detection of one or more of the above features, in a mannerthat is an alternative or supplemental to typical methods of diagnosis.In variations of the specific example, the set of features can, however,include any other suitable features useful for diagnostics.

Characterization of the subject(s) can additionally or alternativelyimplement use of a high false positive test and/or a high false negativetest to further analyze sensitivity of the characterization process insupporting analyses generated according to embodiments of the method100.

Furthermore, in relation to the method(s) described above, a deepsequencing approach can allow for determination of a sufficient numberof copies of DNA sequences to determine relative amount of correspondingbacteria or genetic pathways in the sample. Having identified one ormore of the features described in Sections 1.4.1-1.4.5 above, one cannow diagnose autoimmune conditions in individuals by detecting one ormore of the above features by any quantitative detection method. Forexample, while deep sequencing can be used to detect the presence,absence or amount of one or more option in Sections 1.4.1-1.4.5, one canalso use other detection methods. For example, without intending tolimit the scope of the invention, one could use protein-baseddiagnostics such as immunoassays to detect bacterial taxa by detectingtaxon-specific protein markers.

1.5 First Method: Therapy Models and Provision

As shown in FIG. 1A, in some variations, the first method 100 canfurther include Block S150, which recites: based upon thecharacterization model, generating a therapy model configured to corrector otherwise improve a state of the locomotor system condition. BlockS150 functions to identify or predict therapies (e.g., probiotic-basedtherapies, prebiotic-based therapies, phage-based therapies, smallmolecule-based therapies, etc.) that can shift a subject's microbiomecomposition and/or functional features toward a desired equilibriumstate in promotion of the subject's health. In Block S150, the therapiescan be selected from therapies including one or more of: probiotictherapies, phage-based therapies, prebiotic therapies, smallmolecule-based therapies, cognitive/behavioral therapies, physicalrehabilitation therapies, clinical therapies, medication-basedtherapies, diet-related therapies, and/or any other suitable therapydesigned to operate in any other suitable manner in promoting a user'shealth. In a specific example of a bacteriophage-based therapy, one ormore populations (e.g., in terms of colony forming units) ofbacteriophages specific to a certain bacteria (or other microorganism)represented in a subject with the locomotor system condition can be usedto down-regulate or otherwise eliminate populations of the certainbacteria. As such, bacteriophage-based therapies can be used to reducethe size(s) of the undesired population(s) of bacteria represented inthe subject. Complementarily, bacteriophage-based therapies can be usedto increase the relative abundances of bacterial populations nottargeted by the bacteriophage(s) used.

For instance, in relation to the variations of locomotor systemconditions in Sections 1.4.1 through 1.4.5 above, therapies (e.g.,probiotic therapies, bacteriophage-based therapies, prebiotic therapies,etc.) can be configured to downregulate and/or upregulate microorganismpopulations or subpopulations (and/or functions thereof) associated withfeatures characteristic of the locomotor system condition.

In one such variation, the Block S150 can include one or more of thefollowing steps: obtaining a sample from the subject; purifying nucleicacids (e.g., DNA) from the sample; deep sequencing nucleic acids fromthe sample so as to determine the amount of one or more of the featuresof one or more of Sections 1.4.1-1.4.5; and comparing the resultingamount of each feature to one or more reference amounts of the one ormore of the features listed in one or more of Sections 1.4.1-1.4.5 asoccurs in an average individual having a locomotor system condition oran individual not having the locomotor system condition or both. Thecompilation of features can sometimes be referred to as a “diseasesignature” for a specific disease. The disease signature can act as acharacterization model, and may include probability distributions forcontrol population (no disease) or condition populations having thedisease or both. The disease signature can include one or more of thefeatures (e.g., bacterial taxa or genetic pathways) in the sections andcan optionally include criteria determined from abundance values of thecontrol and/or condition populations. Example criteria can includecutoff or probability values for amounts of those features associatedwith average healthy or diseased individuals.

In a specific example of probiotic therapies, as shown in FIG. 5,candidate therapies of the therapy model can perform one or more of:blocking pathogen entry into an epithelial cell by providing a physicalbarrier (e.g., by way of colonization resistance), inducing formation ofa mucous barrier by stimulation of goblet cells, enhance integrity ofapical tight junctions between epithelial cells of a subject (e.g., bystimulating up regulation of zona-occludens 1, by preventing tightjunction protein redistribution), producing antimicrobial factors,stimulating production of anti-inflammatory cytokines (e.g., bysignaling of dendritic cells and induction of regulatory T-cells),triggering an immune response, and performing any other suitablefunction that adjusts a subject's microbiome away from a state ofdysbiosis.

In variations, the therapy model is preferably based upon data from alarge population of subjects, which can comprise the population ofsubjects from which the microbiome-related datasets are derived in BlockS110, wherein microbiome composition and/or functional features orstates of health, prior exposure to and post exposure to a variety oftherapeutic measures, are well characterized. Such data can be used totrain and validate the therapy provision model, in identifyingtherapeutic measures that provide desired outcomes for subjects basedupon different microbiome characterizations. In variations, supportvector machines, as a supervised machine learning algorithm, can be usedto generate the therapy provision model. However, any other suitablemachine learning algorithm described above can facilitate generation ofthe therapy provision model.

While some methods of statistical analyses and machine learning aredescribed in relation to performance of the Blocks above, variations ofthe method 100 can additionally or alternatively utilize any othersuitable algorithms in performing the characterization process. Invariations, the algorithm(s) can be characterized by a learning styleincluding any one or more of: supervised learning (e.g., using logisticregression, using back propagation neural networks), unsupervisedlearning (e.g., using an Apriori algorithm, using K-means clustering),semi-supervised learning, reinforcement learning (e.g., using aQ-learning algorithm, using temporal difference learning), and any othersuitable learning style. Furthermore, the algorithm(s) can implement anyone or more of: a regression algorithm (e.g., ordinary least squares,logistic regression, stepwise regression, multivariate adaptiveregression splines, locally estimated scatterplot smoothing, etc.), aninstance-based method (e.g., k-nearest neighbor, learning vectorquantization, self-organizing map, etc.), a regularization method (e.g.,ridge regression, least absolute shrinkage and selection operator,elastic net, etc.), a decision tree learning method (e.g.,classification and regression tree, iterative dichotomiser 3, C4.5,chi-squared automatic interaction detection, decision stump, randomforest, multivariate adaptive regression splines, gradient boostingmachines, etc.), a Bayesian method (e.g., naïve Bayes, averagedone-dependence estimators, Bayesian belief network, etc.), a kernelmethod (e.g., a support vector machine, a radial basis function, alinear discriminate analysis, etc.), a clustering method (e.g., k-meansclustering, expectation maximization, etc.), an associated rule learningalgorithm (e.g., an Apriori algorithm, an Eclat algorithm, etc.), anartificial neural network model (e.g., a Perceptron method, aback-propagation method, a Hopfield network method, a self-organizingmap method, a learning vector quantization method, etc.), a deeplearning algorithm (e.g., a restricted Boltzmann machine, a deep beliefnetwork method, a convolution network method, a stacked auto-encodermethod, etc.), a dimensionality reduction method (e.g., principalcomponent analysis, partial lest squares regression, Sammon mapping,multidimensional scaling, projection pursuit, etc.), an ensemble method(e.g., boosting, bootstrapped aggregation, AdaBoost, stackedgeneralization, gradient boosting machine method, random forest method,etc.), and any suitable form of algorithm.

Additionally or alternatively, the therapy model can be derived inrelation to identification of a “normal” or baseline microbiomecomposition and/or functional features, as assessed from subjects of apopulation of subjects who are identified to be in good health. Uponidentification of a subset of subjects of the population of subjects whoare characterized to be in good health (e.g., using features of thecharacterization process), therapies that modulate microbiomecompositions and/or functional features toward those of subjects in goodhealth can be generated in Block S150. Block S150 can thus includeidentification of one or more baseline microbiome compositions and/orfunctional features (e.g., one baseline microbiome for each of a set ofdemographics), and potential therapy formulations and therapy regimensthat can shift microbiomes of subjects who are in a state of dysbiosistoward one of the identified baseline microbiome compositions and/orfunctional features. The therapy model can, however, be generated and/orrefined in any other suitable manner.

Microorganism compositions associated with probiotic therapiesassociated with the therapy model preferably include microorganisms thatare culturable (e.g., able to be expanded to provide a scalable therapy)and non-lethal (e.g., non-lethal in their desired therapeutic dosages).Furthermore, microorganism compositions can comprise a single type ofmicroorganism that has an acute or moderated effect upon a subject'smicrobiome. Additionally or alternatively, microorganism compositionscan comprise balanced combinations of multiple types of microorganismsthat are configured to cooperate with each other in driving a subject'smicrobiome toward a desired state. For instance, a combination ofmultiple types of bacteria in a probiotic therapy can comprise a firstbacteria type that generates products that are used by a second bacteriatype that has a strong effect in positively affecting a subject'smicrobiome. Additionally or alternatively, a combination of multipletypes of bacteria in a probiotic therapy can comprise several bacteriatypes that produce proteins with the same functions that positivelyaffect a subject's microbiome.

In examples of probiotic therapies, probiotic compositions can comprisecomponents of one or more of the identified taxa of microorganisms(e.g., as described in sections 1.4.1 through 1.4.5 above) provided atdosages of 1 million to 10 billion CFUs, as determined from a therapymodel that predicts positive adjustment of a subject's microbiome inresponse to the therapy. Additionally or alternatively, the therapy cancomprise dosages of proteins resulting from functional presence in themicrobiome compositions of subjects without the locomotor systemcondition. In the examples, a subject can be instructed to ingestcapsules comprising the probiotic formulation according to a regimentailored to one or more of his/her: physiology (e.g., body mass index,weight, height), demographics (e.g., gender, age), severity ofdysbiosis, sensitivity to medications, and any other suitable factor.

Furthermore, probiotic compositions of probiotic-based therapies can benaturally or synthetically derived. For instance, in one application, aprobiotic composition can be naturally derived from fecal matter orother biological matter (e.g., of one or more subjects having a baselinemicrobiome composition and/or functional features, as identified usingthe characterization process and the therapy model). Additionally oralternatively, probiotic compositions can be synthetically derived(e.g., derived using a benchtop method) based upon a baseline microbiomecomposition and/or functional features, as identified using thecharacterization process and the therapy model. In variations,microorganism agents that can be used in probiotic therapies can includeone or more of: yeast (e.g., Saccharomyces boulardii), gram-negativebacteria (e.g., E. coli Nissle, Akkermansia muciniphila, Prevotellabryantii, etc.), gram-positive bacteria (e.g., Bifidobacterium animalis(including subspecies lactis), Bifidobacterium longum (includingsubspecies infantis), Bifidobacterium bifidum, Bifidobacteriumpseudolongum, Bifidobacterium thermophilum, Bifidobacterium breve,Lactobacillus rhamnosus, Lactobacillus acidophilus, Lactobacillus casei,Lactobacillus helveticus, Lactobacillus plantarum, Lactobacillusfermentum, Lactobacillus salivarius, Lactobacillus delbrueckii(including subspecies bulgaricus), Lactobacillus johnsonii,Lactobacillus reuteri, Lactobacillus gasseri, Lactobacillus brevis(including subspecies coagulans), Bacillus cereus, Bacillus subtilis(including var. Natto), Bacillus polyfermenticus, Bacillus clausii,Bacillus licheniformis, Bacillus coagulans, Bacillus pumilus,Faecalibacterium prausnitzii, Streptococcus thermophiles, Brevibacillusbrevis, Lactococcus lactis, Leuconostoc mesenteroides, Enterococcusfaecium, Enterococcus faecalis, Enterococcus durans, Clostridiumbutyricum, Sporolactobacillus inulinus, Sporolactobacillus vineae,Pediococcus acidilactic, Pediococcus pentosaceus, etc.), and any othersuitable type of microorganism agent.

Additionally or alternatively, therapies promoted by the therapy modelof Block S150 can include one or more of: consumables (e.g., food items,beverage items, nutritional supplements), suggested activities (e.g.,exercise regimens, adjustments to alcohol consumption, adjustments tocigarette usage, adjustments to drug usage), topical therapies (e.g.,lotions, ointments, antiseptics, etc.), adjustments to hygienic productusage (e.g., use of shampoo products, use of conditioner products, useof soaps, use of makeup products, etc.), adjustments to diet (e.g.,sugar consumption, fat consumption, salt consumption, acid consumption,etc.), adjustments to sleep behavior, living arrangement adjustments(e.g., adjustments to living with pets, adjustments to living withplants in one's home environment, adjustments to light and temperaturein one's home environment, etc.), nutritional supplements (e.g.,vitamins, minerals, fiber, fatty acids, amino acids, prebiotics,probiotics, etc.), medications, antibiotics, and any other suitabletherapeutic measure. Among the prebiotics suitable for treatment, aseither part of any food or as supplement, are included the followingcomponents: 1,4-dihydroxy-2-naphthoic acid (DHNA), Inulin,trans-Galactooligosaccharides (GOS), Lactulose, Mannan oligosaccharides(MOS), Fructooligosaccharides (FOS), Neoagaro-oligosaccharides (NAOS),Pyrodextrins, Xylo-oligosaccharides (XOS), Isomalto-oligosaccharides(IMOS), Amylose-resistant starch, Soybean oligosaccharide (SBOS),Lactitol, Lactosucrose (LS), Isomaltulose (including Palatinose),Arabinoxylooligosaccharides (AXOS), Raffinose oligosaccharides (RFO),Arabinoxylans (AX), Polyphenols or any another compound capable ofchanging the microbiota composition with a desirable effect.

Additionally or alternatively, therapies promoted by the therapy modelof Block S150 can include one or more of: physical rehabilitation;mobility improving therapies; medication (e.g., pain medication, musclerelaxers, etc.); joint extracellular matrix rejuvenating therapies(e.g., hyaluronan injections, collagen treatments, etc.); allographicprocedures to repair joint tissue; autologous procedures to repair jointtissue (e.g., autologous chondrocyte transplantation); microfractureprocedures to promote joint healing with bone-marrow derived stem cells(BMSCs); joint replacement procedures (e.g., hip replacement, shoulderreplacements, temporomandibular joint replacements, knee replacements);joint stabilization replacement procedures; defect repair procedures(e.g., using sutures, using adhesives, using chemical bondingprocedures, etc.); any other suitable medical procedure; therapiesconfigured to increase neuroplasticity in the context of motor ability;transcranial stimulation treatments targeting the motor cortex; stemcell-based neurological therapies; and any other suitable therapy forimproving a state of the locomotor condition of the subject(s).

The first method 100 can, however, include any other suitable blocks orsteps configured to facilitate reception of biological samples fromindividuals, processing of biological samples from individuals,analyzing data derived from biological samples, and generating modelsthat can be used to provide customized diagnostics and/or therapeuticsaccording to specific microbiome compositions of individuals.

1.6 Example Method

Embodiments can provide a method for determining a classification of thepresence or absence for a condition and/or determine a course oftreatment for an individual human having the condition. The method canbe performed by a computer system.

In step 1, sequence reads of bacterial DNA obtained from analyzing atest sample from the individual human are received. The analysis can bedone with various techniques, e.g., as described herein, such assequencing or hybridization arrays. The sequence reads can be receivedat a computer system, e.g., from a detection apparatus, such as asequencing machine that provides data to a storage device (which can beloaded into the computer system) or across a network to the computersystem.

In step 2, the sequence reads are mapped to a bacterial sequencedatabase to obtain a plurality of mapped sequence reads. The bacterialsequence database 60 of 79 includes a plurality of reference sequencesof a plurality of bacteria. The reference sequences can be forpredetermined region(s) of the bacteria, e.g., the 16S region.

In step 3, the mapped sequence reads are assigned to sequence groupsbased on the mapping to obtain assigned sequence reads assigned to atleast one sequence group. A sequence group includes one or more of theplurality of reference sequences. The mapping can involve the sequencereads being mapped to one or more predetermined regions of the referencesequences. For example, the sequence reads can be mapped to the 16Sgene. Thus, the sequence reads do not have to be mapped to the wholegenome, but only to the region(s) covered by the reference sequences ofa sequence group.

In step 4, a total number of assigned sequence reads is determined. Insome embodiments, the total number of assigned reads can include readsidentified as being bacterial, but not assigned to a known sequencegroup. In other embodiments, the total number can be a sum of sequencereads assigned to known sequence groups, where the sum may include anysequence read assigned to at least one sequence group.

In step 5, relative abundance value(s) can be determined. For example,for each sequence group of a disease signature set of one or moresequence groups associated with features described in Sections1.4.1-1.4.5 above, a relative abundance value of assigned sequence readsassigned to the sequence group relative to the total number of assignedsequence reads can be determined. The relative abundance values can forma test feature vector, where each value of the test feature vector is anRAV of a different sequence group.

In step 6, the test feature vector is compared to calibration featurevectors generated from relative abundance values of calibration sampleshaving a known status of the condition. The calibration samples may besamples of a condition population and samples of a control population.In some embodiments, the comparison can involve various machine learningtechniques, such as supervised machine learning (e.g. decision trees,nearest neighbor, support vector machines, neural networks, naïve Bayesclassifier, etc.) and unsupervised machine learning (e.g., clustering,principal component analysis, etc.).

In one embodiment, clustering can use a network approach, where thedistance between each pair of samples in the network is computed basedon the relative abundance of the sequence groups that are relevant foreach condition. Then, a new sample can be compared to all samples in thenetwork, using the same metric based on relative abundance, and it canbe decided to which cluster it should belong. A meaningful distancemetric would allow all diseased individuals to form one or a fewclusters and all healthy individuals to form one or a few clusters. Onedistance metric is the Bray-Curtis dissimilarity, or equivalently asimilarity network, where the metric is 1—Bray-Curtis dissimilarity.Another example distance metric is the Tanimoto coefficient.

In some embodiments, the feature vectors may be compared by transformingthe RAVs into probability values, thereby forming probability vectors.Similar processing for the feature vectors can be performed for theprobability, with such a process still involving a comparison of thefeature vectors since the probability vectors are generated from thefeature vectors.

Step 7 can determine a classification of the presence or absence of thelocomotor system condition and/or determine a course of treatment for anindividual human having locomotor system condition based on thecomparing. For example, the cluster to which the test feature vector isassigned may be a condition cluster, and the classification can be madethat the individual human has the condition or a certain probability forhaving the condition.

In one embodiment involving clustering, the calibration feature vectorscan be clustered into a control cluster not having the condition and acondition cluster having the condition. Then, which cluster the testfeature vector belongs can be determined. The identified cluster can beused to determine the classification or select a course of treatment. Inone implementation, the clustering can use a Bray-Curtis dissimilarity.

In one embodiment involving a decision tree, the comparison may beperformed to by comparing the test feature vector to one or more cutoffvalues (e.g., as a corresponding cutoff vector), where the one or morecutoff values are determined from the calibration feature vectors,thereby providing the comparison. Thus, the comparing can includecomparing each of the relative abundance values of the test featurevector to a respective cutoff value determined from the calibrationfeature vectors generated from the calibration samples. The respectivecutoff values can be determined to provide an optimal discrimination foreach sequence group.

A new sample can be measured to detect the RAVs for the sequence groupsin the disease signature. The RAV for each sequence group can becompared to the probability distributions for the control and conditionspopulations for the particular sequence group. For example, theprobability distribution for the condition population can provide anoutput of a probability (condition probability) of having the conditionfor a given input of the RAV. Similarly, the probability distributionfor the control population can provide an output of a probability(control probability) of not having the condition for a given input ofthe RAV. Thus, the value of the probability distribution at the RAV canprovide the probability of the sample being in each of the populations.Thus, it can be determined which population the sample is more likely tobelong to, by taking the maximum probability.

A total probability across sequence groups of a disease signature can beused. For all of the sequence groups that are measured, a conditionprobability can be determined for whether the sample is in the conditiongroup and a control probability can be determined for whether the sampleis in the control population. In other embodiments, just the conditionprobabilities or just the control probabilities can be determined.

The probabilities across the sequence groups can be used to determine atotal probability. For example, an average of the conditionprobabilities can be determined, thereby obtaining a final conditionprobability of the subject having the condition based on the diseasesignature. An average of the control probabilities can be determined,thereby obtaining a final control probability of the subject not havingthe condition based on the disease signature.

In one embodiment, the final condition probability and final controlprobability can be compared to each other to determine the finalclassification. For instance, a difference between the two finalprobabilities can be determined, and a final classification probabilitydetermined from the difference. A large positive difference with finalcondition probability being higher would result in a higher finalclassification probability of the subject having the disease.

In other embodiments, only the final condition probability can be usedto determine the final classification probability. For example, thefinal classification probability can be the final condition probability.Alternatively, the final classification probability can be one minus thefinal control probability, or 100% minus the final control probabilitydepending on the formatting of the probabilities.

In some embodiments, a final classification probability for one diseaseof a class can be combined with other final classification probabilitiesof other diseases of the same class. The aggregated probability can thenbe used to determine whether the subject has at least one of the classesof diseases. Thus, embodiments can determine whether a subject has ahealth issue that may include a plurality of diseases associated withthat health issue.

The classification can be one of the final probabilities. In otherexamples, embodiments can compare a final probability to a thresholdvalue to make a determination of whether the condition exists. Forexample, the respective condition probabilities can be averaged, and anaverage can be compared to a threshold value to determine whether thecondition exists. As another example, the comparison of the average tothe threshold value can provide a treatment for treating the subject.

2. Method for Generating Microbiome-Derived Diagnostics

In some embodiments, as noted above, outputs of the first method 100 canbe used to generate diagnostics and/or provide therapeutic measures foran individual based upon an analysis of the individual's microbiome. Assuch, a second method 200 derived from at least one output of the firstmethod 100 can include: receiving a biological sample from a subjectS210; characterizing the subject with a form of a locomotor systemcondition based upon processing a microbiome dataset derived from thebiological sample S220; and promoting a therapy to the subject with thelocomotor system condition based upon the characterization and thetherapy model S230.

Block S210 recites: receiving a biological sample from the subject,which functions to facilitate generation of a microbiome compositiondataset and/or a microbiome functional diversity dataset for thesubject. As such, processing and analyzing the biological samplepreferably facilitates generation of a microbiome composition datasetand/or a microbiome functional diversity dataset for the subject, whichcan be used to provide inputs that can be used to characterize theindividual in relation to diagnosis of the locomotor system condition,as in Block S220. Receiving a biological sample from the subject ispreferably performed in a manner similar to that of one of theembodiments, variations, and/or examples of sample reception describedin relation to Block Silo above. As such, reception and processing ofthe biological sample in Block S210 can be performed for the subjectusing similar processes as those for receiving and processing biologicalsamples used to generate the characterization(s) and/or the therapyprovision model of the first method 100, in order to provide consistencyof process. However, biological sample reception and processing in BlockS210 can alternatively be performed in any other suitable manner.

Block S220 recites: characterizing the subject characterizing thesubject with a form of locomotor system condition based upon processinga microbiome dataset derived from the biological sample. Block S220functions to extract features from microbiome-derived data of thesubject, and use the features to positively or negatively characterizethe individual as having a form of the locomotor system condition.Characterizing the subject in Block S220 thus preferably includesidentifying features and/or combinations of features associated with themicrobiome composition and/or functional features of the microbiome ofthe subject, and comparing such features with features characteristic ofsubjects with the locomotor system condition. Block S220 can furtherinclude generation of and/or output of a confidence metric associatedwith the characterization for the individual. For instance, a confidencemetric can be derived from the number of features used to generate theclassification, relative weights or rankings of features used togenerate the characterization, measures of bias in the models used inBlock S140 above, and/or any other suitable parameter associated withaspects of the characterization operation of Block S140.

In some variations, features extracted from the microbiome dataset canbe supplemented with survey-derived and/or medical history-derivedfeatures from the individual, which can be used to further refine thecharacterization operation(s) of Block S220. However, the microbiomecomposition dataset and/or the microbiome functional diversity datasetof the individual can additionally or alternatively be used in any othersuitable manner to enhance the first method 100 and/or the second method200.

Block S230 recites: promoting a therapy to the subject with thelocomotor system condition based upon the characterization and thetherapy model. Block S230 functions to recommend or provide apersonalized therapeutic measure to the subject, in order to shift themicrobiome composition of the individual toward a desired equilibriumstate. As such, Block S230 can include correcting the locomotor systemcondition, or otherwise positively affecting the user's health inrelation to the locomotor system condition. Block S230 can thus includepromoting one or more therapeutic measures to the subject based upontheir characterization in relation to the locomotor system condition, asdescribed in relation to Sections 1.4.1 through 1.4.5 above, wherein thetherapy is configured to modulate taxonomic makeup of the subject'smicrobiome and/or modulate functional feature aspects of the subject ina desired manner toward a “normal” state in relation to thecharacterizations described above.

In Block S230, providing the therapeutic measure to the subject caninclude recommendation of available therapeutic measures configured tomodulate microbiome composition of the subject toward a desired state.Additionally or alternatively, Block S230 can include provision ofcustomized therapy to the subject according to their characterization(e.g., in relation to a specific type of locomotor system condition). Invariations, therapeutic measures for adjusting a microbiome compositionof the subject, in order to improve a state of the locomotor systemcondition can include one or more of: probiotics, prebiotics,bacteriophage-based therapies, consumables, suggested activities,topical therapies, adjustments to hygienic product usage, adjustments todiet, adjustments to sleep behavior, living arrangement, adjustments tolevel of sexual activity, nutritional supplements, medications,antibiotics, and any other suitable therapeutic measure. Therapyprovision in Block S230 can include provision of notifications by way ofan electronic device, through an entity associated with the individual,and/or in any other suitable manner.

In more detail, therapy provision in Block S230 can include provision ofnotifications to the subject regarding recommended therapeutic measuresand/or other courses of action, in relation to health-related goals, asshown in FIG. 6. Notifications can be provided to an individual by wayof an electronic device (e.g., personal computer, mobile device, tablet,head-mounted wearable computing device, wrist-mounted wearable computingdevice, etc.) that executes an application, web interface, and/ormessaging client configured for notification provision. In one example,a web interface of a personal computer or laptop associated with asubject can provide access, by the subject, to a user account of thesubject, wherein the user account includes information regarding thesubject's characterization, detailed characterization of aspects of thesubject's microbiome composition and/or functional features, andnotifications regarding suggested therapeutic measures generated inBlock S150. In another example, an application executing at a personalelectronic device (e.g., smart phone, smart watch, head-mounted smartdevice) can be configured to provide notifications (e.g., at a display,haptically, in an auditory manner, etc.) regarding therapeuticsuggestions generated by the therapy model of Block S150. Notificationscan additionally or alternatively be provided directly through an entityassociated with a subject (e.g., a caretaker, a spouse, a significantother, a healthcare professional, etc.). In some further variations,notifications can additionally or alternatively be provided to an entity(e.g., healthcare professional) associated with the subject, wherein theentity is able to administer the therapeutic measure (e.g., by way ofprescription, by way of conducting a therapeutic session, etc.).Notifications can, however, be provided for therapy administration tothe subject in any other suitable manner.

Furthermore, in an extension of Block S230, monitoring of the subjectduring the course of a therapeutic regimen (e.g., by receiving andanalyzing biological samples from the subject throughout therapy, byreceiving survey-derived data from the subject throughout therapy) canbe used to generate a therapy-effectiveness model for each recommendedtherapeutic measure provided according to the model generated in BlockS150.

The methods 100, 200 and/or system of the embodiments can be embodiedand/or implemented at least in part as a machine configured to receive acomputer-readable medium storing computer-readable instructions. Theinstructions can be executed by computer-executable componentsintegrated with the application, applet, host, server, network, website,communication service, communication interface,hardware/firmware/software elements of a patient computer or mobiledevice, or any suitable combination thereof. Other systems and methodsof the embodiments can be embodied and/or implemented at least in partas a machine configured to receive a computer-readable medium storingcomputer-readable instructions. The instructions can be executed bycomputer-executable components integrated with apparatuses and networksof the type described above. The computer-readable medium can be storedon any suitable computer readable media such as RAMs, ROMs, flashmemory, EEPROMs, optical devices (CD or DVD), hard drives, floppydrives, or any suitable device. The computer-executable component can bea processor, though any suitable dedicated hardware device can(alternatively or additionally) execute the instructions.

The FIGURES illustrate the architecture, functionality and operation ofpossible implementations of systems, methods and computer programproducts according to preferred embodiments, example configurations, andvariations thereof. In this regard, each block in the flowchart or blockdiagrams may represent a module, segment, step, or portion of code,which comprises one or more executable instructions for implementing thespecified logical function(s). It should also be noted that, in somealternative implementations, the functions noted in the block can occurout of the order noted in the FIGURES. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reverse order, depending uponthe functionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts, or combinations of special purpose hardware andcomputer instructions.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the embodiments of the invention without departing fromthe scope of this invention as defined in the following claims.

We claim:
 1. A method for at least one of characterizing, diagnosing,and treating a locomotor system condition in at least a subject, themethod comprising: at a sample handling network, processing nucleic acidcontent of each of an aggregate set of samples, from a population ofsubjects, with a fragmentation operation, a multiplexed amplificationoperation using a set of primers, a sequencing analysis operation, andan alignment operation; at a computing system in communication with thesample handling network, generating a microbiome composition dataset anda microbiome functional diversity dataset upon processing of theaggregate set of samples; at the computing system, receiving asupplementary dataset informative of the locomotor system conditionwithin the population of subjects; at the computing system, transformingthe supplementary dataset and features extracted from the microbiomecomposition dataset and the microbiome functional diversity dataset intoa characterization model of the locomotor system condition; generating atherapy model, associated with the characterization model, configured toimprove a state of the locomotor system condition; and at an outputdevice associated with the subject, promoting a therapy to the subject,upon characterizing the subject with the locomotor system conditionaccording to the characterization model and the therapy model.
 2. Themethod of claim 1, wherein generating the characterization modelcomprises performing a statistical analysis to assess a set ofmicrobiome composition features and microbiome functional featureshaving variations across a first subset of the population of subjectsexhibiting the locomotor system condition and a second subset of thepopulation of subjects not exhibiting the locomotor system condition. 3.The method of claim 2, wherein generating the characterization modelcomprises: extracting candidate features associated with a set offunctional aspects of microbiome components indicated in the microbiomecomposition dataset to generate the microbiome functional diversitydataset; and characterizing the locomotor system condition inassociation with a subset of the set of functional aspects, the subsetderived from at least one of clusters of orthologous groups of proteinsfeatures, genomic functional features from the Kyoto Encyclopedia ofGenes and Genomes (KEGG), genomic functional features from a Cluster ofOrthologous Groups (COG) database, chemical functional features, andsystemic functional features.
 4. The method of claim 3, whereingenerating the characterization model of the locomotor system conditioncomprises generating a characterization that is diagnostic of anarthritic disorder.
 5. The method of claim 4, wherein generating thecharacterization model of the locomotor system condition comprisesgenerating the characterization of at least one of: gout, rheumatoidarthritis, and reactive arthritis.
 6. The method of claim 3, whereingenerating the characterization model of the locomotor system conditioncomprises generating a characterization that is diagnostic of aneuromuscular skeletal system disorder.
 7. The method of claim 6,wherein generating the characterization model of the locomotor systemcondition comprises generating the characterization of at least one of:multiple sclerosis and Parkinson's disease.
 8. The method of claim 7,wherein generating the characterization that is diagnostic of multiplesclerosis comprises generating the characterization upon processing theaggregate set of samples and determining presence of features derivedfrom at least one of 1) a set of taxa including Lactococcus (genus), and2) a set of functions associated with at least one of a KyotoEncyclopedia of Genes and Genomes (KEGG) functional feature and aclusters of orthologous groups (COG) functional feature.
 9. The methodof claim 3, wherein generating the characterization model of thelocomotor system condition comprises generating a characterization thatis diagnostic of a musculoskeletal system disorder.
 10. A method forcharacterizing a locomotor system condition, the method comprising: uponprocessing an aggregate set of samples from a population of subjects,generating at least one of a microbiome composition dataset and amicrobiome functional diversity dataset for the population of subjects,the microbiome functional diversity dataset indicative of systemicfunctions present in the microbiome components of the aggregate set ofsamples; at the computing system, transforming features extracted fromat least one of the microbiome composition dataset and the microbiomefunctional diversity dataset into a characterization model of thelocomotor system condition, wherein the characterization is diagnosticof at least one of gout, rheumatoid arthritis, and reactive arthritis;and generating a therapy model, associated with the characterizationmodel, configured to improve a state of the locomotor system condition.11. The method of claim 10, wherein generating the characterizationcomprises analyzing a set of features from the microbiome compositiondataset with a statistical analysis, wherein the set of featuresincludes features associated with: relative abundance of differenttaxonomic groups represented in the microbiome composition dataset. 12.The method of claim 10, wherein generating the characterization modelcomprises analyzing a set of features form the microbiome compositiondataset with a statistical analysis, wherein the set of featuresincludes features associated with interactions between differenttaxonomic groups represented in the microbiome composition dataset andphylogenetic distance between taxonomic groups represented in themicrobiome composition dataset.
 13. The method of claim 10, whereingenerating the characterization comprises performing a statisticalanalysis with at least one of a Kolmogorov-Smirnov test and a Welch'st-test to assess a set of microbiome composition features and microbiomefunctional features having variations across a first subset of thepopulation of subjects exhibiting the locomotor system condition and asecond subset of the population of subjects not exhibiting the locomotorsystem condition.
 14. The method of claim 10, wherein generating thecharacterization model comprises generating a diagnostic of an arthriticdisorder from features extracted from at the microbiome compositiondataset and the microbiome functional diversity dataset.
 15. The methodof claim 14, wherein generating the characterization model furthercomprises generating a diagnostic of a neuromuscular skeletal systemdisorder.
 16. The method of claim 15, wherein generating the diagnosticof the neuromuscular skeletal system disorder comprises generating thediagnostic of at least one of multiple sclerosis and Parkinson'sdisease.
 17. The method of claim 10, further including diagnosing asubject with the locomotor system condition upon processing a samplefrom the subject according to the characterization model; and at anoutput device associated with the subject, promoting a therapy to thesubject based upon the therapy model.
 18. The method of claim 17,wherein promoting the therapy comprises promoting a bacteriophage-basedtherapy to the subject, the bacteriophage-based therapy providing abacteriophage component that selectively downregulates a population sizeof an undesired taxon associated with the locomotor system condition.19. The method of claim 17, wherein promoting the therapy comprisespromoting at least one of a probiotic and a prebiotic therapy to thesubject, the therapy including a consumable component affecting amicroorganism component that selectively modulates one or more of taxaand functions associated with correction of the locomotor systemcondition, based on the therapy model.
 20. The method of claim 17,wherein promoting the therapy comprises promoting a microbiomecomposition modifying therapy to the subject in order to improve a stateof the locomotor system condition.
 21. A method for characterizing alocomotor system condition in a subject, the method comprising: uponprocessing an aggregate set of samples from a population of subjects,generating a microbiome composition dataset and a microbiome functionaldiversity dataset for the population of subjects, the microbiomefunctional diversity dataset indicative of systemic functions present inthe microbiome components of the aggregate set of samples, whereingenerating the microbiome functional diversity dataset comprisesextracting candidate features associated with a set of functionalaspects of microbiome components indicated in the microbiome compositiondataset; and transforming a subset of the set of functional aspects intoa characterization model of the locomotor system condition, the subsetderived from at least one of clusters of orthologous groups of proteinsfeatures, genomic functional features from a Kyoto Encyclopedia of Genesand Genomes (KEGG) database, genomic functional features from a Clusterof Orthologous Groups (COG) database, chemical functional features, andsystemic functional features.
 22. The method of claim 21, whereingenerating the characterization model of the locomotor system conditioncomprises characterizing at least one of gout, rheumatoid arthritis, andreactive arthritis.